Why USAA? At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the choice for the military community and their families. Embrace a fulfilling career at USAA, where our core values - honesty, integrity, loyalty and service - define how we treat each other and our members. Be part of what truly makes us special and impactful. We are proud to support active-duty military spouses. USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with applicable policy and business needs. The Opportunity As a dedicated AI Data Solutions Scientist in the Technology organization at USAA, you will work within our innovative Data Science team and collaborate cross-functionally with our architecture, engineering, and product partners to transform our operations and experiences while producing actionable insights to drive our association forward. As a member of our dynamic community of problem-solvers, you will tackle a broad and evolving spectrum of business targets to provide outstanding impacts for our membership through scaled solutions and cloud technologies, leveraging both structured and unstructured data through traditional pillars of operations research such as simulation, optimization, and machine-learning techniques, as well as a heavy emphasis on cutting-edge technologies with generative AI, large/small language models, and advanced agent frameworks. This team is the backbone of the next generation of AI modeling at USAA, and we hope you join us on the frontier! We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, Plano, TX, or Phoenix, AZ. Relocation assistance is not available for this position. What you'll do: Gather, interpret, and manipulate structured and unstructured data to enable advanced analytical solutions for the business. Develop scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value. Select the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs. Develop and deploy models within the Model Development Control (MDC) and Model Risk Management (MRM) framework. Composes, and assists peers with composing, technical documents for knowledge persistence, risk management, and technical review audiences. Assess business needs to propose/recommend analytical and modeling projects to add business value. Work with business and analytics leaders to prioritize analytics and modeling problems/research efforts. Build and maintain a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data. Translate complex business request(s) into specific analytical questions, executes on the analysis and/or modeling, and then communicates outcomes to non-technical business colleagues with focus on business action and recommendations. Manage project milestones, risks, and impediments. Escalates potential issues that could limit project success or implementation. Develop best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards. Maintain expertise and awareness of cutting-edge techniques. Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies. Serve as a mentor to junior data scientists in modeling, analytics, and computer science tasks. Participate in internal communities that drive the maintenance and transformation of data science technologies and culture. Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures. What you have: Bachelor's degree in mathematics, Computer Science, Statistics, Science, Engineering, or quantitative field; OR 4 years of relevant education and/or experience; and 6+ years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master's, PhD) in mathematics, computer science, statistics, science and engineering, ai, or other similar quantitative discipline and 4+ years of experience in predictive analytics or data analysis. 4+ years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models. 4+ years of experience in Python for performing statical analysis and/or building and scoring AI/ML models Experience writing code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency). Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc. Demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics and understanding real-world constraints such as latency, cost, and reliability in AI solution designs. Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts across risk stripes, including experience in the documentation and statistical validation of models for risk management. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc. Expertise in LLMs and agentic systems development with frameworks such as LangChain/LangGraph, AgentCore, VertexAI, MCP, or others, with proven experience including prompt engineering, tuning and post-training techniques, multi-agent systems, agent optimization and tool use, RAG and context optimization, and observability and monitoring. MLOps Integration experience in facilitating engineering implementation of production scaled AI solutions in partnership with dedicated AI Engineers in cloud environments such as AWS or GCP. Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results. Experience guiding and mentoring junior technical staff in business interactions and model building. What sets you apart: Financial services, insurance, banking, or other highly regulated industry experience. Experience with cloud-native application development and modernization initiatives. US military experience through military service or a military spouse/domestic partner Compensation range: The salary range for this position is: $143,320 - $273,930. USAA does not provide visa sponsorship for this role. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H-1B, TN, STEM OPT Training Plans, etc.). Compensation: USAA has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market data of the position. The actual salary for this role may vary by location. Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors. The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job. Benefits: At USAA our employees enjoy best-in-class benefits to support their physical, financial, and emotional wellness. These benefits include comprehensive medical, dental and vision plans, 401(k), pension, life insurance, parental benefits, adoption assistance, paid time off program with paid holidays plus 16 paid volunteer hours, and various wellness programs. Additionally, our career path planning and continuing education assists employees with their professional goals. For more details on our outstanding benefits, visit our benefits page on Applications for this position are accepted on an ongoing basis, this posting will remain open until the position is filled. Thus, interested candidates are encouraged to apply the same day they view this posting. USAA is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
08/24/2026
Full time
Why USAA? At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the choice for the military community and their families. Embrace a fulfilling career at USAA, where our core values - honesty, integrity, loyalty and service - define how we treat each other and our members. Be part of what truly makes us special and impactful. We are proud to support active-duty military spouses. USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with applicable policy and business needs. The Opportunity As a dedicated AI Data Solutions Scientist in the Technology organization at USAA, you will work within our innovative Data Science team and collaborate cross-functionally with our architecture, engineering, and product partners to transform our operations and experiences while producing actionable insights to drive our association forward. As a member of our dynamic community of problem-solvers, you will tackle a broad and evolving spectrum of business targets to provide outstanding impacts for our membership through scaled solutions and cloud technologies, leveraging both structured and unstructured data through traditional pillars of operations research such as simulation, optimization, and machine-learning techniques, as well as a heavy emphasis on cutting-edge technologies with generative AI, large/small language models, and advanced agent frameworks. This team is the backbone of the next generation of AI modeling at USAA, and we hope you join us on the frontier! We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, Plano, TX, or Phoenix, AZ. Relocation assistance is not available for this position. What you'll do: Gather, interpret, and manipulate structured and unstructured data to enable advanced analytical solutions for the business. Develop scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value. Select the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs. Develop and deploy models within the Model Development Control (MDC) and Model Risk Management (MRM) framework. Composes, and assists peers with composing, technical documents for knowledge persistence, risk management, and technical review audiences. Assess business needs to propose/recommend analytical and modeling projects to add business value. Work with business and analytics leaders to prioritize analytics and modeling problems/research efforts. Build and maintain a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data. Translate complex business request(s) into specific analytical questions, executes on the analysis and/or modeling, and then communicates outcomes to non-technical business colleagues with focus on business action and recommendations. Manage project milestones, risks, and impediments. Escalates potential issues that could limit project success or implementation. Develop best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards. Maintain expertise and awareness of cutting-edge techniques. Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies. Serve as a mentor to junior data scientists in modeling, analytics, and computer science tasks. Participate in internal communities that drive the maintenance and transformation of data science technologies and culture. Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures. What you have: Bachelor's degree in mathematics, Computer Science, Statistics, Science, Engineering, or quantitative field; OR 4 years of relevant education and/or experience; and 6+ years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master's, PhD) in mathematics, computer science, statistics, science and engineering, ai, or other similar quantitative discipline and 4+ years of experience in predictive analytics or data analysis. 4+ years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models. 4+ years of experience in Python for performing statical analysis and/or building and scoring AI/ML models Experience writing code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency). Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc. Demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics and understanding real-world constraints such as latency, cost, and reliability in AI solution designs. Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts across risk stripes, including experience in the documentation and statistical validation of models for risk management. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc. Expertise in LLMs and agentic systems development with frameworks such as LangChain/LangGraph, AgentCore, VertexAI, MCP, or others, with proven experience including prompt engineering, tuning and post-training techniques, multi-agent systems, agent optimization and tool use, RAG and context optimization, and observability and monitoring. MLOps Integration experience in facilitating engineering implementation of production scaled AI solutions in partnership with dedicated AI Engineers in cloud environments such as AWS or GCP. Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results. Experience guiding and mentoring junior technical staff in business interactions and model building. What sets you apart: Financial services, insurance, banking, or other highly regulated industry experience. Experience with cloud-native application development and modernization initiatives. US military experience through military service or a military spouse/domestic partner Compensation range: The salary range for this position is: $143,320 - $273,930. USAA does not provide visa sponsorship for this role. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H-1B, TN, STEM OPT Training Plans, etc.). Compensation: USAA has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market data of the position. The actual salary for this role may vary by location. Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors. The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job. Benefits: At USAA our employees enjoy best-in-class benefits to support their physical, financial, and emotional wellness. These benefits include comprehensive medical, dental and vision plans, 401(k), pension, life insurance, parental benefits, adoption assistance, paid time off program with paid holidays plus 16 paid volunteer hours, and various wellness programs. Additionally, our career path planning and continuing education assists employees with their professional goals. For more details on our outstanding benefits, visit our benefits page on Applications for this position are accepted on an ongoing basis, this posting will remain open until the position is filled. Thus, interested candidates are encouraged to apply the same day they view this posting. USAA is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Why USAA? At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the choice for the military community and their families. Embrace a fulfilling career at USAA, where our core values - honesty, integrity, loyalty and service - define how we treat each other and our members. Be part of what truly makes us special and impactful. We are proud to support active-duty military spouses. USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with applicable policy and business needs. The Opportunity The Senior Artificial Intelligence and Machine Learning Engineer will be part of a dedicated AI engineering team that focuses on developing and implementing AI/ML solutions for all lines of business within USAA. This role provides excellent opportunities to learn and contribute to a variety of exciting areas including sentiment analysis and agentic AI applications to enable USAA to provide best in class service to our members with cutting edge technology. Responsibilities include data preprocessing, model training, building APIs, and building data pipelines. We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, or Plano, TX. Relocation assistance is not available for this position. What you'll do: Consults with data scientists and business partners on approaches involving ML model development and implementation. Provides technical feedback on ML solutions within technical lead community. Leads the full life cycle of machine learning engineering to include analysis, solution design, data pipeline engineering, testing, deployment, scheduling, application integration, production support, API development, and application integration in support of GenAI applications, ML frameworks/libraires, and ML models. Configure, manage, and set up AI/ML infrastructure components in cloud/on-prem environments for projects and the AI/ML community stakeholders. This includes AWS, GCP, graph databases. Works with architects to influence and define ML model implementation patterns in complex environments. Understand complex model implementation requirements and how to recognize and apply appropriate design patterns throughout solution design and implementation. Designs and implements complex technical solutions for machine learning, artificial intelligence, GenAI, Creates proof of concepts and prototypes on highly complex solutions and presents to leadership to deliver on vision for solution. Assists in setting technical direction for the team and serves as a mentor to team members. Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures. What you have: Bachelor's degree; OR 4 years of relevant education and/or experience; and 6+ years of machine learning engineering, data engineering, or software development experience implementing data solutions. Extensive programming experience using Python, SQL, Java etc. Extensive data engineering experience. Experience with end to end delivering of machine learning and AI/GenAI solutions that includes building, integrating, and optimizing data pipelines; curating and modeling the data for various types of consumers/products and their pattern of usage to include data marts, data lake, and operational analytic applications. Experience completing multiple projects leveraging the agile methodology in implementing ML models. Experience leveraging ML Ops principles and implementing automated workflows. Demonstrated technical leadership experience Demonstrated ability to mentor junior members of the technical team in area of expertise. Knowledge of Data Science principles and methodologies. Knowledge of stochastic modeling, machine learning, or other advanced mathematical techniques. Effective communication skills, with the ability to present complex technical concepts to both technical and non-technical audiences. Expert knowledge of at least one cloud platform and relevant components e.g., AWS Sagemaker. What sets you apart: Programming skills using FastAPI, OpenShift, Snowflake (or any similar platform), Java, etc. Financial services, insurance, banking, or other highly regulated industry experience. Experience mentoring junior developers and providing technical leadership. US military experience through military service or a military spouse/domestic partner Compensation range: The salary range for this position is: $143,320 - $273,930. USAA does not provide visa sponsorship for this role. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H-1B, TN, STEM OPT Training Plans, etc.). Compensation: USAA has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market data of the position. The actual salary for this role may vary by location. Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors. The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job. Benefits: At USAA our employees enjoy best-in-class benefits to support their physical, financial, and emotional wellness. These benefits include comprehensive medical, dental and vision plans, 401(k), pension, life insurance, parental benefits, adoption assistance, paid time off program with paid holidays plus 16 paid volunteer hours, and various wellness programs. Additionally, our career path planning and continuing education assists employees with their professional goals. For more details on our outstanding benefits, visit our benefits page on Applications for this position are accepted on an ongoing basis, this posting will remain open until the position is filled. Thus, interested candidates are encouraged to apply the same day they view this posting. USAA is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
08/24/2026
Full time
Why USAA? At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the choice for the military community and their families. Embrace a fulfilling career at USAA, where our core values - honesty, integrity, loyalty and service - define how we treat each other and our members. Be part of what truly makes us special and impactful. We are proud to support active-duty military spouses. USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with applicable policy and business needs. The Opportunity The Senior Artificial Intelligence and Machine Learning Engineer will be part of a dedicated AI engineering team that focuses on developing and implementing AI/ML solutions for all lines of business within USAA. This role provides excellent opportunities to learn and contribute to a variety of exciting areas including sentiment analysis and agentic AI applications to enable USAA to provide best in class service to our members with cutting edge technology. Responsibilities include data preprocessing, model training, building APIs, and building data pipelines. We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, or Plano, TX. Relocation assistance is not available for this position. What you'll do: Consults with data scientists and business partners on approaches involving ML model development and implementation. Provides technical feedback on ML solutions within technical lead community. Leads the full life cycle of machine learning engineering to include analysis, solution design, data pipeline engineering, testing, deployment, scheduling, application integration, production support, API development, and application integration in support of GenAI applications, ML frameworks/libraires, and ML models. Configure, manage, and set up AI/ML infrastructure components in cloud/on-prem environments for projects and the AI/ML community stakeholders. This includes AWS, GCP, graph databases. Works with architects to influence and define ML model implementation patterns in complex environments. Understand complex model implementation requirements and how to recognize and apply appropriate design patterns throughout solution design and implementation. Designs and implements complex technical solutions for machine learning, artificial intelligence, GenAI, Creates proof of concepts and prototypes on highly complex solutions and presents to leadership to deliver on vision for solution. Assists in setting technical direction for the team and serves as a mentor to team members. Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures. What you have: Bachelor's degree; OR 4 years of relevant education and/or experience; and 6+ years of machine learning engineering, data engineering, or software development experience implementing data solutions. Extensive programming experience using Python, SQL, Java etc. Extensive data engineering experience. Experience with end to end delivering of machine learning and AI/GenAI solutions that includes building, integrating, and optimizing data pipelines; curating and modeling the data for various types of consumers/products and their pattern of usage to include data marts, data lake, and operational analytic applications. Experience completing multiple projects leveraging the agile methodology in implementing ML models. Experience leveraging ML Ops principles and implementing automated workflows. Demonstrated technical leadership experience Demonstrated ability to mentor junior members of the technical team in area of expertise. Knowledge of Data Science principles and methodologies. Knowledge of stochastic modeling, machine learning, or other advanced mathematical techniques. Effective communication skills, with the ability to present complex technical concepts to both technical and non-technical audiences. Expert knowledge of at least one cloud platform and relevant components e.g., AWS Sagemaker. What sets you apart: Programming skills using FastAPI, OpenShift, Snowflake (or any similar platform), Java, etc. Financial services, insurance, banking, or other highly regulated industry experience. Experience mentoring junior developers and providing technical leadership. US military experience through military service or a military spouse/domestic partner Compensation range: The salary range for this position is: $143,320 - $273,930. USAA does not provide visa sponsorship for this role. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H-1B, TN, STEM OPT Training Plans, etc.). Compensation: USAA has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market data of the position. The actual salary for this role may vary by location. Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors. The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job. Benefits: At USAA our employees enjoy best-in-class benefits to support their physical, financial, and emotional wellness. These benefits include comprehensive medical, dental and vision plans, 401(k), pension, life insurance, parental benefits, adoption assistance, paid time off program with paid holidays plus 16 paid volunteer hours, and various wellness programs. Additionally, our career path planning and continuing education assists employees with their professional goals. For more details on our outstanding benefits, visit our benefits page on Applications for this position are accepted on an ongoing basis, this posting will remain open until the position is filled. Thus, interested candidates are encouraged to apply the same day they view this posting. USAA is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Stop Guessing What Employers Want - Start Delivering It! No Interviews or Offers? Get Hired with a Process! Most job seekers guess what employers want, but SynergisticIT shows you exactly what companies are hiring for right now. You'll work on the most in demand technologies, build real world projects, and receive interview preparation tailored to current industry expectations. Also you will get your profile directly to Fortune 500 clients ensuring your skills reach the right people. Whether you're a new graduate or someone returning after a gap,Synergisticit helps you align your abilities with employer needs. When you stop guessing and start delivering, getting hired becomes much easier. In today's market, employers expect more than a degree or a few tutorial projects. They want candidates who look job-ready on paper, sound confident in interviews, and demonstrate hands-on ability in the tools teams actually use. That's exactly what SynergisticIT solves-because the real challenge isn't learning in isolation. The real challenge is translating learning into interviews and offers. Since 2010, SynergisticIT has helped thousands of candidates secure full-time roles with leading companies and recognizable brands-think Google, Apple, PayPal, Visa, Western Union, Wells Fargo, Intel, Walmart Labs, Citi, JPMC, Bank of America, Wayfair, and many more-often in the $95k to $154k offer range depending on role, location, and skillset. The purpose of SynergisticIT is simple: close the gap between what you know and what employers expect you to prove. Here's the truth employers hire based on whether you can handle real work-clean coding, debugging, teamwork workflows, version control, APIs, cloud basics, deployment pipelines, and the ability to explain what you did. That's why SynergisticIT emphasizes structured skill-building, project depth, resume positioning, interview readiness, and support through the job-search process. What roles are in demand right now? A lot of jobseekers assume they must become "AI experts" overnight. Not true. Many companies are actively hiring professionals in core roles that run modern software teams. In JOPP, the demand typically includes roles such as entry-level software programmer, Java full stack developer, Python/Java developer, data analyst, data engineer, data scientist, and machine learning/AI engineer. In other words, SynergisticIT focuses on building candidates across Java / Full Stack / DevOps and Data Analytics / Data Engineering / Data Science / ML/AI based on what employers repeatedly request. Who benefits most from this model? If you're applying and not seeing results, you're likely in one of these situations: You have skills, but your resume doesn't show impact and your projects look generic You know tools, but you can't explain them confidently in interviews You've learned from courses, but you lack real-world structure and job alignment You've built a portfolio, but it doesn't match what hiring managers evaluate SynergisticIT works especially well for candidates such as: recent grads in CS/Engineering/Math/Stats, jobseekers who were laid off and need an updated stack, career switchers who want a guided plan, candidates with career gaps, people with "learning but not hired" bootcamp history, experienced professionals not landing interviews, and international candidates on F1/OPT needing a clear employment pathway. SynergisticIT also supports candidates with guidance around STEM extension, and provides process support for H-1B and Green Card filing once employed (as applicable through employers and standard processes). If you want to explore here are the key links: please read our blogs Why do Tech Companies not Hire recent Computer Science Graduates Technical Skills or Experience? Which one is important to get a Job? Please check below links: Synergisticit Industry Event videos (OCW, JavaOne, Gartner data Analytics): USA Today feature Discover JOPP: Contact: You don't need more random applications. You need a job-ready plan. Start smarter-start with the right support. Please read our blogs Why do Tech Companies not Hire recent Computer Science Graduates SynergisticIT What Recruiters Look for in Junior Developers SynergisticIT Software engineering or Data Science as a career? Please note: Resume databases are shared with clients and interested clients will reach out directly if they find a qualified candidate for their req. Resume submissions may be shared with our JOPP team database also. Please unsubscribe if contacted or if you don't want to be contacted please don't submit your resume.
08/24/2026
Full time
Stop Guessing What Employers Want - Start Delivering It! No Interviews or Offers? Get Hired with a Process! Most job seekers guess what employers want, but SynergisticIT shows you exactly what companies are hiring for right now. You'll work on the most in demand technologies, build real world projects, and receive interview preparation tailored to current industry expectations. Also you will get your profile directly to Fortune 500 clients ensuring your skills reach the right people. Whether you're a new graduate or someone returning after a gap,Synergisticit helps you align your abilities with employer needs. When you stop guessing and start delivering, getting hired becomes much easier. In today's market, employers expect more than a degree or a few tutorial projects. They want candidates who look job-ready on paper, sound confident in interviews, and demonstrate hands-on ability in the tools teams actually use. That's exactly what SynergisticIT solves-because the real challenge isn't learning in isolation. The real challenge is translating learning into interviews and offers. Since 2010, SynergisticIT has helped thousands of candidates secure full-time roles with leading companies and recognizable brands-think Google, Apple, PayPal, Visa, Western Union, Wells Fargo, Intel, Walmart Labs, Citi, JPMC, Bank of America, Wayfair, and many more-often in the $95k to $154k offer range depending on role, location, and skillset. The purpose of SynergisticIT is simple: close the gap between what you know and what employers expect you to prove. Here's the truth employers hire based on whether you can handle real work-clean coding, debugging, teamwork workflows, version control, APIs, cloud basics, deployment pipelines, and the ability to explain what you did. That's why SynergisticIT emphasizes structured skill-building, project depth, resume positioning, interview readiness, and support through the job-search process. What roles are in demand right now? A lot of jobseekers assume they must become "AI experts" overnight. Not true. Many companies are actively hiring professionals in core roles that run modern software teams. In JOPP, the demand typically includes roles such as entry-level software programmer, Java full stack developer, Python/Java developer, data analyst, data engineer, data scientist, and machine learning/AI engineer. In other words, SynergisticIT focuses on building candidates across Java / Full Stack / DevOps and Data Analytics / Data Engineering / Data Science / ML/AI based on what employers repeatedly request. Who benefits most from this model? If you're applying and not seeing results, you're likely in one of these situations: You have skills, but your resume doesn't show impact and your projects look generic You know tools, but you can't explain them confidently in interviews You've learned from courses, but you lack real-world structure and job alignment You've built a portfolio, but it doesn't match what hiring managers evaluate SynergisticIT works especially well for candidates such as: recent grads in CS/Engineering/Math/Stats, jobseekers who were laid off and need an updated stack, career switchers who want a guided plan, candidates with career gaps, people with "learning but not hired" bootcamp history, experienced professionals not landing interviews, and international candidates on F1/OPT needing a clear employment pathway. SynergisticIT also supports candidates with guidance around STEM extension, and provides process support for H-1B and Green Card filing once employed (as applicable through employers and standard processes). If you want to explore here are the key links: please read our blogs Why do Tech Companies not Hire recent Computer Science Graduates Technical Skills or Experience? Which one is important to get a Job? Please check below links: Synergisticit Industry Event videos (OCW, JavaOne, Gartner data Analytics): USA Today feature Discover JOPP: Contact: You don't need more random applications. You need a job-ready plan. Start smarter-start with the right support. Please read our blogs Why do Tech Companies not Hire recent Computer Science Graduates SynergisticIT What Recruiters Look for in Junior Developers SynergisticIT Software engineering or Data Science as a career? Please note: Resume databases are shared with clients and interested clients will reach out directly if they find a qualified candidate for their req. Resume submissions may be shared with our JOPP team database also. Please unsubscribe if contacted or if you don't want to be contacted please don't submit your resume.
The Job Market Isn't Slow - It's Selective Get Job Offers with a Process which works! Many job seekers think the tech market is dead, but the truth is companies are hiring selectively. They want candidates who are trained, polished, and ready to contribute immediately. SynergisticIT prepares you exactly for that. You'll gain hands on experience, master in demand technologies, and receive interview coaching that helps you outperform other applicants. Plus direct access to 28000+ contacts at different employers. If you're tired of waiting for the market to "get better," become the candidate companies are already looking for. You've done a ton of Leetcode. You've racked up certificates, done LeetCode challenges, and you know your way around system design like the back of your hand. On paper, you're everything a tech company wants. However tech stacks and requirements change every day. Companies are looking for Employees who can contribute on Projects from Day 1. So what is needed is the right tech stack and the right project work. Companies have options and as a Jobseeker you can have options also if you have the right tech stack and Project work Since 2010, we've helped thousands of candidates land full-time jobs at tech leaders like Google, Apple, PayPal, Visa, Western Union, Wells Fargo, Intel, Paypal, JPMC, Wayfair, BOA, CITI and hundreds more with Job offers of $95k to $154k. Synergisticit focuses on closing the gap between your tech skills and what employers want now. All visa types and U.S. citizens are encouraged to apply. We Focus on Java /Full stack/Devops and Data Science /Data Engineers/Data analysts/BI Analysts/ Machine learning/AI candidates Ideal Candidates: Recent grads in CS, Engineering, Math, or Statistics with limited or no job experience Jobseekers who had layoffs due to Downsizing and want to get in demand tech stack Professionals seeking a career switch to tech Candidates with career gaps or lacking real-world experience Individuals looking to boost their skill portfolio for better job prospects Computer Science grads with limited or no job experience Students who recently finished their Bachelor's or Master's programs Those struggling to land interviews despite having experience Currently, We are looking for entry-level software programmers, Java Full stack developers, Python/Java developers, Data analysts/Data Engineers/ Data Scientists, Machine Learning engineers for full time positions with clients. Top tech companies are flooded with smart grads. What gets you in the door now is real-world application, confidence in delivery, and the soft skills to own a room-or a Zoom. Please check below links: Event videos (OCW, JavaOne, Gartner): USA Today feature Discover JOPP: Contact: please read our blogs Why do Tech Companies not Hire recent Computer Science Graduates Technical Skills or Experience? Which one is important to get a Job? The Market's Changed-Have You? Please note: Resume databases are shared with clients and interested clients will reach out directly if they find a qualified candidate for their req. Resume submissions may be shared with our JOPP team database also. Please unsubscribe if contacted or if you don't want to be contacted please don't submit your resume.
08/24/2026
Full time
The Job Market Isn't Slow - It's Selective Get Job Offers with a Process which works! Many job seekers think the tech market is dead, but the truth is companies are hiring selectively. They want candidates who are trained, polished, and ready to contribute immediately. SynergisticIT prepares you exactly for that. You'll gain hands on experience, master in demand technologies, and receive interview coaching that helps you outperform other applicants. Plus direct access to 28000+ contacts at different employers. If you're tired of waiting for the market to "get better," become the candidate companies are already looking for. You've done a ton of Leetcode. You've racked up certificates, done LeetCode challenges, and you know your way around system design like the back of your hand. On paper, you're everything a tech company wants. However tech stacks and requirements change every day. Companies are looking for Employees who can contribute on Projects from Day 1. So what is needed is the right tech stack and the right project work. Companies have options and as a Jobseeker you can have options also if you have the right tech stack and Project work Since 2010, we've helped thousands of candidates land full-time jobs at tech leaders like Google, Apple, PayPal, Visa, Western Union, Wells Fargo, Intel, Paypal, JPMC, Wayfair, BOA, CITI and hundreds more with Job offers of $95k to $154k. Synergisticit focuses on closing the gap between your tech skills and what employers want now. All visa types and U.S. citizens are encouraged to apply. We Focus on Java /Full stack/Devops and Data Science /Data Engineers/Data analysts/BI Analysts/ Machine learning/AI candidates Ideal Candidates: Recent grads in CS, Engineering, Math, or Statistics with limited or no job experience Jobseekers who had layoffs due to Downsizing and want to get in demand tech stack Professionals seeking a career switch to tech Candidates with career gaps or lacking real-world experience Individuals looking to boost their skill portfolio for better job prospects Computer Science grads with limited or no job experience Students who recently finished their Bachelor's or Master's programs Those struggling to land interviews despite having experience Currently, We are looking for entry-level software programmers, Java Full stack developers, Python/Java developers, Data analysts/Data Engineers/ Data Scientists, Machine Learning engineers for full time positions with clients. Top tech companies are flooded with smart grads. What gets you in the door now is real-world application, confidence in delivery, and the soft skills to own a room-or a Zoom. Please check below links: Event videos (OCW, JavaOne, Gartner): USA Today feature Discover JOPP: Contact: please read our blogs Why do Tech Companies not Hire recent Computer Science Graduates Technical Skills or Experience? Which one is important to get a Job? The Market's Changed-Have You? Please note: Resume databases are shared with clients and interested clients will reach out directly if they find a qualified candidate for their req. Resume submissions may be shared with our JOPP team database also. Please unsubscribe if contacted or if you don't want to be contacted please don't submit your resume.
Position: Technical Coach Location: Fort Worth, Texas Duration: Contract Job ID: 178612 Job Overview: We are seeking a highly skilled and experienced Technical Coach to join our team in Fort Worth, Texas. This role involves working onsite three days a week (Tuesday through Thursday) and offers the opportunity to convert to a full-time employee. The ideal candidate will have a strong background in software development, coaching, and modern engineering practices, with a focus on AI, machine learning, and agile methodologies. This position is part of a high-performing IT organization dedicated to delivering exceptional digital products and fostering a culture of technical excellence. Responsibilities: Coach teams in an immersive dojo/coaching space to enhance their technical capabilities. Evaluate product portfolios and assess architecture and engineering opportunities. Facilitate Value Stream Mapping sessions to identify development and operational improvements. Identify opportunities to increase workflow efficiency and eliminate waste. Define and curate metrics to measure progress toward desired outcomes. Serve as an expert on modern technology architecture, engineering, and DevOps practices. Collaborate with product teams to improve customer delivery and achieve company objectives. Mentor and work hands-on with software teams, including engineers and architects. Lead large pairing/mobbing sessions to enhance learning and focus. Design and develop training materials related to modern engineering practices. Embed with teams to understand their environment and design impactful coaching interventions. Develop and implement coaching strategies for teams, including software engineers and architects. Contribute to the developer experience platform to streamline continuous delivery of value. Qualifications: Bachelor's degree in Computer Science or a related discipline, or equivalent work experience. 8+ years of experience in software development and coaching. Hands-on experience with AI, machine learning, and modern engineering practices. Proficiency in object-oriented programming languages such as Java, Python, or C#. Experience with TDD, BDD, and Agile methodologies like Extreme Programming, Kanban, or Scrum. Expertise in Continuous Integration and Continuous Delivery (CI/CD). Proficiency in DevOps tools and platforms, including Kubernetes, Docker, and GitHub Actions. Experience with cloud providers such as Azure and AWS. Strong communication skills and the ability to mentor and guide teams effectively. Proven experience in SQL and designing relational database schemas. Experience with modern and classical neural networks, including LLMs, CNNs, and RNNs. About PTR Global: PTR Global is a leading provider of information technology and workforce solutions. PTR Global has become one of the largest providers in its industry, with over 5000 professionals providing services across the U.S. and Canada. For more information visit At PTR Global, we understand the importance of your privacy and security. We NEVER ASK job applicants to: Pay any fee to be considered for, submitted to, or selected for any opportunity. Purchase any product, service, or gift cards from us or for us as part of an application, interview, or selection process. Provide sensitive financial information such as credit card numbers or banking information. Successfully placed or hired candidates would only be asked for banking details after accepting an offer from us during our official onboarding processes as part of payroll setup. Pay Range: $80 - $85/Hr on W2 The specific compensation for this position will be determined by several factors, including the scope, complexity, and location of the role, as well as the cost of labor in the market; the skills, education, training, credentials, and experience of the candidate; and other conditions of employment. Our full-time consultants have access to benefits, including medical, dental, vision, and 401K contributions, as well as PTO, sick leave, and other benefits mandated by applicable state or localities where you reside or work. If you receive a suspicious message, email, or phone call claiming to be from PTR Global do not respond or click on any links. Instead, contact us directly at +1 . To report any concerns, please email us at During the hiring process, we may use artificial intelligence (AI) tools to assist in evaluating information related to your application. These tools may help analyze job-related qualifications or assist recruiters in reviewing applications and interview responses. AI-generated information is one factor considered in our hiring process. Final employment decisions are made by qualified hiring personnel and are not based solely on AI-generated recommendations.
08/24/2026
Full time
Position: Technical Coach Location: Fort Worth, Texas Duration: Contract Job ID: 178612 Job Overview: We are seeking a highly skilled and experienced Technical Coach to join our team in Fort Worth, Texas. This role involves working onsite three days a week (Tuesday through Thursday) and offers the opportunity to convert to a full-time employee. The ideal candidate will have a strong background in software development, coaching, and modern engineering practices, with a focus on AI, machine learning, and agile methodologies. This position is part of a high-performing IT organization dedicated to delivering exceptional digital products and fostering a culture of technical excellence. Responsibilities: Coach teams in an immersive dojo/coaching space to enhance their technical capabilities. Evaluate product portfolios and assess architecture and engineering opportunities. Facilitate Value Stream Mapping sessions to identify development and operational improvements. Identify opportunities to increase workflow efficiency and eliminate waste. Define and curate metrics to measure progress toward desired outcomes. Serve as an expert on modern technology architecture, engineering, and DevOps practices. Collaborate with product teams to improve customer delivery and achieve company objectives. Mentor and work hands-on with software teams, including engineers and architects. Lead large pairing/mobbing sessions to enhance learning and focus. Design and develop training materials related to modern engineering practices. Embed with teams to understand their environment and design impactful coaching interventions. Develop and implement coaching strategies for teams, including software engineers and architects. Contribute to the developer experience platform to streamline continuous delivery of value. Qualifications: Bachelor's degree in Computer Science or a related discipline, or equivalent work experience. 8+ years of experience in software development and coaching. Hands-on experience with AI, machine learning, and modern engineering practices. Proficiency in object-oriented programming languages such as Java, Python, or C#. Experience with TDD, BDD, and Agile methodologies like Extreme Programming, Kanban, or Scrum. Expertise in Continuous Integration and Continuous Delivery (CI/CD). Proficiency in DevOps tools and platforms, including Kubernetes, Docker, and GitHub Actions. Experience with cloud providers such as Azure and AWS. Strong communication skills and the ability to mentor and guide teams effectively. Proven experience in SQL and designing relational database schemas. Experience with modern and classical neural networks, including LLMs, CNNs, and RNNs. About PTR Global: PTR Global is a leading provider of information technology and workforce solutions. PTR Global has become one of the largest providers in its industry, with over 5000 professionals providing services across the U.S. and Canada. For more information visit At PTR Global, we understand the importance of your privacy and security. We NEVER ASK job applicants to: Pay any fee to be considered for, submitted to, or selected for any opportunity. Purchase any product, service, or gift cards from us or for us as part of an application, interview, or selection process. Provide sensitive financial information such as credit card numbers or banking information. Successfully placed or hired candidates would only be asked for banking details after accepting an offer from us during our official onboarding processes as part of payroll setup. Pay Range: $80 - $85/Hr on W2 The specific compensation for this position will be determined by several factors, including the scope, complexity, and location of the role, as well as the cost of labor in the market; the skills, education, training, credentials, and experience of the candidate; and other conditions of employment. Our full-time consultants have access to benefits, including medical, dental, vision, and 401K contributions, as well as PTO, sick leave, and other benefits mandated by applicable state or localities where you reside or work. If you receive a suspicious message, email, or phone call claiming to be from PTR Global do not respond or click on any links. Instead, contact us directly at +1 . To report any concerns, please email us at During the hiring process, we may use artificial intelligence (AI) tools to assist in evaluating information related to your application. These tools may help analyze job-related qualifications or assist recruiters in reviewing applications and interview responses. AI-generated information is one factor considered in our hiring process. Final employment decisions are made by qualified hiring personnel and are not based solely on AI-generated recommendations.
Job Overview: Pay Range: $75hr - $80hr Requirement/Must Have: 10+ years of experience in AI and MLOps. Strong Python programming skills. Experience with machine learning frameworks (TensorFlow, PyTorch, Scikit-learn). Knowledge of SQL and data processing. Familiarity with cloud services (AWS, Azure, or GCP). Exposure to MLOps tools such as MLflow, Kubeflow, Docker, Kubernetes, Git, or Jenkins. Strong analytical and problem-solving skills. Responsibilities: Design, develop, and deploy machine learning models. Build data pipelines and perform data preprocessing and feature engineering. Develop AI/ML solutions using Python, TensorFlow, PyTorch, or Scikit-learn. Work with cloud platforms (AWS, Azure, or GCP) to deploy ML solutions. Implement basic MLOps practices, including model deployment, monitoring, versioning, and CI/CD for ML workflows. Monitor model performance and retrain models as needed. Collaborate with data scientists, software engineers, and business stakeholders. Benefits Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
08/24/2026
Full time
Job Overview: Pay Range: $75hr - $80hr Requirement/Must Have: 10+ years of experience in AI and MLOps. Strong Python programming skills. Experience with machine learning frameworks (TensorFlow, PyTorch, Scikit-learn). Knowledge of SQL and data processing. Familiarity with cloud services (AWS, Azure, or GCP). Exposure to MLOps tools such as MLflow, Kubeflow, Docker, Kubernetes, Git, or Jenkins. Strong analytical and problem-solving skills. Responsibilities: Design, develop, and deploy machine learning models. Build data pipelines and perform data preprocessing and feature engineering. Develop AI/ML solutions using Python, TensorFlow, PyTorch, or Scikit-learn. Work with cloud platforms (AWS, Azure, or GCP) to deploy ML solutions. Implement basic MLOps practices, including model deployment, monitoring, versioning, and CI/CD for ML workflows. Monitor model performance and retrain models as needed. Collaborate with data scientists, software engineers, and business stakeholders. Benefits Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
Job Description Summary As a GE Vernova accelerator, GE Vernova Advanced Research is driving strategy and leading research & development efforts to execute on the business's mission to power the energy transition. We forge the partnerships and invent the technologies required to electrify and decarbonize for a zero-carbon future. Representing every major scientific and engineering discipline, our researchers are collaborating with GE Vernova's businesses, the U.S. government, and more than 420 technology partners to execute on 150+ energy-focused projects. Collectively, these research programs and initiatives aim to solve near term technical challenges, deliver next generation product advances, and drive long term breakthrough innovation to enable more affordable, reliable, sustainable, and secure energy. Job Description As Control Systems Research Engineer, you will enable Energy Transition through the development of models, advanced controls, optimization, estimation, and detection technologies. You will contribute to R&D programs aligned with GE Vernova's energy businesses such as Renewable Energy, Gas Power, Grid Solutions, and Power Conversion as well as with U.S. Government Agencies such as the Department of Energy and the Department of Defense. The technologies developed under these programs will be matured and transitioned into GE business products where they drive breakthrough impact for our company, our customers, and our society. As a Control Systems Research Engineer, you will: Work independently as well as in diverse teams to develop and apply advanced technology solutions to GE Vernova products and services using modeling, advanced controls, optimization, estimation, and detection technologies. Validate performance of developed solutions through simulations and application on target systems. Document technology and results through patent applications, technical reports, and publications. Stay current with advances in system technologies to seek out new ideas and applications. Work in a team environment with colleagues across GE Research, GE Vernova, and partners from industry, academia, and government agencies. Position Requirements Doctorate degree in an Engineering or related field with experience in design of advanced controls, optimization, estimation, or detection algorithms or a Master's degree in an Engineering or related field with a minimum of 3 years of experience in design of advanced controls, optimization, estimation, or detection algorithms. 3+ years of knowledge and application of advanced controls, optimization, estimation, or detection algorithms. 3+ years of proficiency in MATLAB/Simulink and C, C++, or Python. 3+ years of demonstrated experience (algorithm development and software implementation) in developing systems solutions for complex physical systems (e.g., mechanical systems, chemical plants, power systems, transportation, aviation systems). Desired Qualifications Active/current U.S. security clearance Experience with machine learning and artificial intelligence techniques and the application of those in a controls context Experience with real-time implementation of system solutions in a variety of hardware platforms Experience developing controls solutions for industrial applications Experience with machine learning and artificial intelligence techniques and the application of those in a controls context Experience developing cyber-security algorithms Exposure to industrial control hardware programming and industrial control software development Proficiency in source code management through git/github Experience with application for government funding and project proposal writing Experience with program management, which includes setting up schedules, milestones, technical reviews, tracking funding, etc. Demonstrated ability to take an innovative idea from a concept to a product Eligibility Requirements Legal authorization to work in the U.S. is required. We will not sponsor individuals at the Masters level for employment visas, now or in the future, for this job opening. Must be willing to work out of an office located in Niskayuna, NY. Ability to maintain a U.S. security clearance, prerequisite for clearance is U.S. citizenship. GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation. GE Vernova is an Equal Opportunity Employer . Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law. GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable). Relocation Assistance Provided: Yes For candidates applying to a U.S. based position, the pay range for this position is between $89,300.00 and $148,700.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate's experience, education, and skill set. Bonus eligibility: ineligible. This posting is expected to remain open for at least seven days after it was posted on June 01, 2026. Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off. GE Vernova Inc. or its affiliates (collectively or individually, "GE Vernova") sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.
08/24/2026
Full time
Job Description Summary As a GE Vernova accelerator, GE Vernova Advanced Research is driving strategy and leading research & development efforts to execute on the business's mission to power the energy transition. We forge the partnerships and invent the technologies required to electrify and decarbonize for a zero-carbon future. Representing every major scientific and engineering discipline, our researchers are collaborating with GE Vernova's businesses, the U.S. government, and more than 420 technology partners to execute on 150+ energy-focused projects. Collectively, these research programs and initiatives aim to solve near term technical challenges, deliver next generation product advances, and drive long term breakthrough innovation to enable more affordable, reliable, sustainable, and secure energy. Job Description As Control Systems Research Engineer, you will enable Energy Transition through the development of models, advanced controls, optimization, estimation, and detection technologies. You will contribute to R&D programs aligned with GE Vernova's energy businesses such as Renewable Energy, Gas Power, Grid Solutions, and Power Conversion as well as with U.S. Government Agencies such as the Department of Energy and the Department of Defense. The technologies developed under these programs will be matured and transitioned into GE business products where they drive breakthrough impact for our company, our customers, and our society. As a Control Systems Research Engineer, you will: Work independently as well as in diverse teams to develop and apply advanced technology solutions to GE Vernova products and services using modeling, advanced controls, optimization, estimation, and detection technologies. Validate performance of developed solutions through simulations and application on target systems. Document technology and results through patent applications, technical reports, and publications. Stay current with advances in system technologies to seek out new ideas and applications. Work in a team environment with colleagues across GE Research, GE Vernova, and partners from industry, academia, and government agencies. Position Requirements Doctorate degree in an Engineering or related field with experience in design of advanced controls, optimization, estimation, or detection algorithms or a Master's degree in an Engineering or related field with a minimum of 3 years of experience in design of advanced controls, optimization, estimation, or detection algorithms. 3+ years of knowledge and application of advanced controls, optimization, estimation, or detection algorithms. 3+ years of proficiency in MATLAB/Simulink and C, C++, or Python. 3+ years of demonstrated experience (algorithm development and software implementation) in developing systems solutions for complex physical systems (e.g., mechanical systems, chemical plants, power systems, transportation, aviation systems). Desired Qualifications Active/current U.S. security clearance Experience with machine learning and artificial intelligence techniques and the application of those in a controls context Experience with real-time implementation of system solutions in a variety of hardware platforms Experience developing controls solutions for industrial applications Experience with machine learning and artificial intelligence techniques and the application of those in a controls context Experience developing cyber-security algorithms Exposure to industrial control hardware programming and industrial control software development Proficiency in source code management through git/github Experience with application for government funding and project proposal writing Experience with program management, which includes setting up schedules, milestones, technical reviews, tracking funding, etc. Demonstrated ability to take an innovative idea from a concept to a product Eligibility Requirements Legal authorization to work in the U.S. is required. We will not sponsor individuals at the Masters level for employment visas, now or in the future, for this job opening. Must be willing to work out of an office located in Niskayuna, NY. Ability to maintain a U.S. security clearance, prerequisite for clearance is U.S. citizenship. GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation. GE Vernova is an Equal Opportunity Employer . Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law. GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable). Relocation Assistance Provided: Yes For candidates applying to a U.S. based position, the pay range for this position is between $89,300.00 and $148,700.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate's experience, education, and skill set. Bonus eligibility: ineligible. This posting is expected to remain open for at least seven days after it was posted on June 01, 2026. Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off. GE Vernova Inc. or its affiliates (collectively or individually, "GE Vernova") sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.
Software Engineering Institute | Carnegie Mellon University
Pittsburgh, Pennsylvania
Job Description: Are you a cybersecurity and/or AI researcher who enjoys a challenge? Are you excited about pioneering new research areas that will impact academia, industry, and national security? If so, we want you for our team, where you'll collaborate to deliver high-quality results in the emerging area of AI security. The CERT Division of the Software Engineering Institute (SEI) is seeking applicants for the AI Security Researcher role. Originally created in response to one of the first computer viruses the Morris worm - in 1988, CERT has remained a leader in cybersecurity research, improving the robustness of software systems, and in responding to sophisticated cybersecurity threats. Ensuring the robustness and security of AI systems is the next big challenge on the horizon, and we are seeking life-long learners in the fields of cybersecurity, AI/ML, or related areas, who are willing to cross-train to address AI Security. As part of the Threat Analysis Directorate, you will join a group of security experts focused on advancing the state of the art in AI security at a national and global scale. Our tasks include vulnerability discovery and assessments for AI systems, evaluation of the effectiveness and robustness of defenses and mitigations for AI systems, reverse engineering AI systems and models, and identifying new areas where security research is needed. We participate in communities of network defenders, software developers and vendors, security researchers, AI practitioners, and policymakers. You'll get a chance to work with elite AI and cybersecurity professionals, university faculty, and government representatives to build new methodologies and technologies that will influence national AI security strategy for decades to come. You will co-author research proposals, execute studies, and present findings and recommendations to our DoW sponsors, decision makers within government and industry, and at academic conferences. The SEI is a non-profit, federally funded research and development center (FFRDC) at Carnegie Mellon University. What you'll do: Develop state of the art approaches for analyzing robustness of AI systems. Apply these approaches to understanding vulnerabilities in AI systems and how attackers adapt their tradecraft to exploit those vulnerabilities. Reverse engineer malicious code in support of high-impact customers, design and develop new analysis methods and tools, work to identify and address emerging and complex threats to AI systems and effectively participate in the broader security community. Study and influence the AI security and vulnerability disclosure ecosystems. Evaluate the effectiveness of tools, techniques and processes developed by industry and the AI security research community. Uncover and shape some of the fundamental assumptions underlying current best practice in AI security. Develop thought models, tools and data sets that can be used to characterize the threats to, and vulnerabilities in, AI systems, and publish those results. You will also use these results to aid in the testing, evaluation and transition of technologies developed by government-funded research programs. Identify opportunities to apply AI to improve existing cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with eight (8) years of experience; OR MS in the same fields with five (5) years of experience; OR PhD in the same fields with two (2) years of experience. You have a deep interest in AI/ML and cybersecurity with a penchant for intellectual curiosity and a desire to make an impact beyond your organization. You have practical experience with applying cybersecurity knowledge toward vulnerability research, analysis, disclosure, or mitigation. You have experience with advising on a range of security topics based on research and expert opinion. You have familiarity with implementing and applying AI/ML techniques to solving practical problems. You have familiarity with common AI/ML software packages and tools (e.g., Numpy, Pytorch, Tensorflow, ART). You have knowledge or familiarity with reverse engineering tools (e.g. NSA Ghidra, IDA Pro) You have experience with Python, C/C++, or low-level programming. You have experience developing frameworks, methodologies, or assessments to evaluate effectiveness and robustness of technologies. You have excellent communication skills (oral and written), particularly regarding technical communications with non-experts. You enjoy mentoring and cross-training others and sharing knowledge within the broader community. Candidates with strong technical proficiency in either AI/ML or cybersecurity are welcome to apply, provided a demonstrated intellectual agility and commitment required for accelerated learning within the role. You are able to: Travel to various locations to support the SEI's overall mission. This includes within the SEI and CMU community, sponsor sites, conferences, and offsite meetings on occasion (5%). You will be subject to a background check and will need to obtain and maintain a Department of War (DoW) security clearance. Why work here? Join a world-class organization that continues to have a significant impact on software. Work with cutting-edge technologies and dedicated experts to solve tough problems for the government and the nation. Be surrounded by friendly and knowledgeable staff with broad expertise across AI/ML, cybersecurity, software engineering, risk management, and policy creation. Get 8% monthly contribution for your retirement, without having to contribute yourself. Get tuition benefits to CMU and other institutions for you and your dependent children. Enjoy a healthy work/life balance with flexible work arrangements and paid parental and military leave. Enjoy annual professional development opportunities; attend conferences and training or obtain a certification and get reimbursed for membership in professional societies. Qualify for relocation assistance and so much more.
08/23/2026
Full time
Job Description: Are you a cybersecurity and/or AI researcher who enjoys a challenge? Are you excited about pioneering new research areas that will impact academia, industry, and national security? If so, we want you for our team, where you'll collaborate to deliver high-quality results in the emerging area of AI security. The CERT Division of the Software Engineering Institute (SEI) is seeking applicants for the AI Security Researcher role. Originally created in response to one of the first computer viruses the Morris worm - in 1988, CERT has remained a leader in cybersecurity research, improving the robustness of software systems, and in responding to sophisticated cybersecurity threats. Ensuring the robustness and security of AI systems is the next big challenge on the horizon, and we are seeking life-long learners in the fields of cybersecurity, AI/ML, or related areas, who are willing to cross-train to address AI Security. As part of the Threat Analysis Directorate, you will join a group of security experts focused on advancing the state of the art in AI security at a national and global scale. Our tasks include vulnerability discovery and assessments for AI systems, evaluation of the effectiveness and robustness of defenses and mitigations for AI systems, reverse engineering AI systems and models, and identifying new areas where security research is needed. We participate in communities of network defenders, software developers and vendors, security researchers, AI practitioners, and policymakers. You'll get a chance to work with elite AI and cybersecurity professionals, university faculty, and government representatives to build new methodologies and technologies that will influence national AI security strategy for decades to come. You will co-author research proposals, execute studies, and present findings and recommendations to our DoW sponsors, decision makers within government and industry, and at academic conferences. The SEI is a non-profit, federally funded research and development center (FFRDC) at Carnegie Mellon University. What you'll do: Develop state of the art approaches for analyzing robustness of AI systems. Apply these approaches to understanding vulnerabilities in AI systems and how attackers adapt their tradecraft to exploit those vulnerabilities. Reverse engineer malicious code in support of high-impact customers, design and develop new analysis methods and tools, work to identify and address emerging and complex threats to AI systems and effectively participate in the broader security community. Study and influence the AI security and vulnerability disclosure ecosystems. Evaluate the effectiveness of tools, techniques and processes developed by industry and the AI security research community. Uncover and shape some of the fundamental assumptions underlying current best practice in AI security. Develop thought models, tools and data sets that can be used to characterize the threats to, and vulnerabilities in, AI systems, and publish those results. You will also use these results to aid in the testing, evaluation and transition of technologies developed by government-funded research programs. Identify opportunities to apply AI to improve existing cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with eight (8) years of experience; OR MS in the same fields with five (5) years of experience; OR PhD in the same fields with two (2) years of experience. You have a deep interest in AI/ML and cybersecurity with a penchant for intellectual curiosity and a desire to make an impact beyond your organization. You have practical experience with applying cybersecurity knowledge toward vulnerability research, analysis, disclosure, or mitigation. You have experience with advising on a range of security topics based on research and expert opinion. You have familiarity with implementing and applying AI/ML techniques to solving practical problems. You have familiarity with common AI/ML software packages and tools (e.g., Numpy, Pytorch, Tensorflow, ART). You have knowledge or familiarity with reverse engineering tools (e.g. NSA Ghidra, IDA Pro) You have experience with Python, C/C++, or low-level programming. You have experience developing frameworks, methodologies, or assessments to evaluate effectiveness and robustness of technologies. You have excellent communication skills (oral and written), particularly regarding technical communications with non-experts. You enjoy mentoring and cross-training others and sharing knowledge within the broader community. Candidates with strong technical proficiency in either AI/ML or cybersecurity are welcome to apply, provided a demonstrated intellectual agility and commitment required for accelerated learning within the role. You are able to: Travel to various locations to support the SEI's overall mission. This includes within the SEI and CMU community, sponsor sites, conferences, and offsite meetings on occasion (5%). You will be subject to a background check and will need to obtain and maintain a Department of War (DoW) security clearance. Why work here? Join a world-class organization that continues to have a significant impact on software. Work with cutting-edge technologies and dedicated experts to solve tough problems for the government and the nation. Be surrounded by friendly and knowledgeable staff with broad expertise across AI/ML, cybersecurity, software engineering, risk management, and policy creation. Get 8% monthly contribution for your retirement, without having to contribute yourself. Get tuition benefits to CMU and other institutions for you and your dependent children. Enjoy a healthy work/life balance with flexible work arrangements and paid parental and military leave. Enjoy annual professional development opportunities; attend conferences and training or obtain a certification and get reimbursed for membership in professional societies. Qualify for relocation assistance and so much more.
Job Description Description Leidos is seeking a Systems Engineer to support a complex, multi-year contract providing Systems Engineering and Integration support to DISA. The work performed on this Leidos-led prime contract is mission-critical and offers team members the opportunity to work autonomously while developing expertise in cross domain technologies. The Systems Engineer will support a high priority, high visibility development, integration, and implementation effort for a mission-critical DISA Cross Domain program. The selected candidate will contribute to multiple concurrent tasks and collaborate across engineering and mission teams. The successful candidate will be a motivated, forward-leaning engineer with strong technical fundamentals and the ability to grow into more senior responsibilities within a dynamic customer environment. The ideal candidate demonstrates a commitment to delivering high-quality work, continuous learning, and effective collaboration within a team environment. This individual is expected to quickly come up to speed, operate with a high degree of autonomy, and proactively contribute to mission objectives. The role requires the ability to adapt to evolving priorities in a dynamic, fast-paced environment while maintaining attention to detail and technical excellence. Primary Responsibilities include: Support decomposition of customer and system requirements in alignment with cross-domain roadmaps. Assist in the design, implementation, and maintenance of cross domain systems and infrastructure, including servers, networks, storage systems, and software applications. Strong understanding to configure and maintain physical and virtual environments, including VMware and ESXi systems. Apply and validate system security configurations, including DISA STIGs (Security Technical Implementation Guides), to ensure compliance with security requirements. Perform hands-on hardware and software configuration of systems, including network configuration and cabling. Collaborate with stakeholders to gather requirements and support development of technical solutions aligned with mission and security objectives. Install, configure, and troubleshoot IT system components to ensure stability and performance. Work with customers, mission partners, program managers, and other technical team members to support system integration activities. Assist in the creation and maintenance of automation scripts (e.g. Ansible, Python, Bash, etc ) to streamline deployments, support internal lab environment projects and perfective maintenance priorities of the program. Provide support to system infrastructure, network security, and Authorization & Accreditation (A&A) activities. Develop and maintain technical documentation and assist with Engineering Implementation Plans. Support integration, testing, verification, and validation activities for systems and components. Track hardware, software, and configuration changes to support documentation updates and system baselines. Provide input to training materials and support knowledge sharing across the team. Basic Qualifications: Active Secret clearance is required; active TS clearance preferred. Bachelor's degree in computer science, engineering, or related field with 2-4 years of relevant experience (or equivalent combination of education and experience). Demonstrated full life cycle, systems integration experience. Strong demonstrated experience with VMware and ESXi environments. Hands-on experience with hardware and software configuration of physical systems, including network configuration and cabling for physical devices and networking concepts. Experience supporting development of DoDAF architecture frameworks to inform systems engineering design and implementation best practices utilizing MS Visio. Experience troubleshooting operational systems, including participation in advanced (Tier III-level) troubleshooting and root cause analysis for deployed environments. Working knowledge of software development or scripting (e.g., XML, Python, Bash,PowerShell, Java, C++). Familiarity with system development processes, databases, and IT infrastructure concepts. Linux command line experience with certification such as CompTIA Linux+ or RHEL certification with understanding of virtual machine architecture. Must possess DoD 8140 (formerly 8570) IAT Level II certification (e.g., Security+) prior to start date and maintain certification. Willingness to travel CONUS and OCONUS up to 10% of the time. Demonstrated ability to deliver high-quality technical work, rapidly learn new technologies, and effectively collaborate with team members to support mission objectives in a dynamic environment. Ability to quickly ramp up on new systems and processes, work independently with minimal oversight, and effectively contribute in a fast-paced, evolving operational environment while maintaining high standards of quality and teamwork. Preferred Qualifications: Active TS clearance. Familiarity with containerization technologies, such as Docker and OpenShift. Cisco Certified Network Associate (CCNA) certification. Experience supporting end-to-end cross domain systems development or integration efforts. Familiarity with engineering best practices such as Raise the Bar Version 5. If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares. Original Posting:August 17, 2026 For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above. Pay Range:Pay Range $69,550.00 - $125,725.00 The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law. About Leidos Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. Headquartered in Reston, Virginia, with 47,000 global employees, Leidos reported annual revenues of approximately $16.7 billion for the fiscal year ended January 3, 2025. For more information, visit . Pay and Benefits Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available at -benefits. Securing Your Data Beware of fake employment opportunities using Leidos' name. Leidos will never ask you to provide payment-related information during any part of the employment application process (i.e., ask you for money), nor will Leidos ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Leidos will only communicate with you through emails that are generated by the automated system - never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Leidos that asks for payment-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at . If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission. Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.
08/23/2026
Full time
Job Description Description Leidos is seeking a Systems Engineer to support a complex, multi-year contract providing Systems Engineering and Integration support to DISA. The work performed on this Leidos-led prime contract is mission-critical and offers team members the opportunity to work autonomously while developing expertise in cross domain technologies. The Systems Engineer will support a high priority, high visibility development, integration, and implementation effort for a mission-critical DISA Cross Domain program. The selected candidate will contribute to multiple concurrent tasks and collaborate across engineering and mission teams. The successful candidate will be a motivated, forward-leaning engineer with strong technical fundamentals and the ability to grow into more senior responsibilities within a dynamic customer environment. The ideal candidate demonstrates a commitment to delivering high-quality work, continuous learning, and effective collaboration within a team environment. This individual is expected to quickly come up to speed, operate with a high degree of autonomy, and proactively contribute to mission objectives. The role requires the ability to adapt to evolving priorities in a dynamic, fast-paced environment while maintaining attention to detail and technical excellence. Primary Responsibilities include: Support decomposition of customer and system requirements in alignment with cross-domain roadmaps. Assist in the design, implementation, and maintenance of cross domain systems and infrastructure, including servers, networks, storage systems, and software applications. Strong understanding to configure and maintain physical and virtual environments, including VMware and ESXi systems. Apply and validate system security configurations, including DISA STIGs (Security Technical Implementation Guides), to ensure compliance with security requirements. Perform hands-on hardware and software configuration of systems, including network configuration and cabling. Collaborate with stakeholders to gather requirements and support development of technical solutions aligned with mission and security objectives. Install, configure, and troubleshoot IT system components to ensure stability and performance. Work with customers, mission partners, program managers, and other technical team members to support system integration activities. Assist in the creation and maintenance of automation scripts (e.g. Ansible, Python, Bash, etc ) to streamline deployments, support internal lab environment projects and perfective maintenance priorities of the program. Provide support to system infrastructure, network security, and Authorization & Accreditation (A&A) activities. Develop and maintain technical documentation and assist with Engineering Implementation Plans. Support integration, testing, verification, and validation activities for systems and components. Track hardware, software, and configuration changes to support documentation updates and system baselines. Provide input to training materials and support knowledge sharing across the team. Basic Qualifications: Active Secret clearance is required; active TS clearance preferred. Bachelor's degree in computer science, engineering, or related field with 2-4 years of relevant experience (or equivalent combination of education and experience). Demonstrated full life cycle, systems integration experience. Strong demonstrated experience with VMware and ESXi environments. Hands-on experience with hardware and software configuration of physical systems, including network configuration and cabling for physical devices and networking concepts. Experience supporting development of DoDAF architecture frameworks to inform systems engineering design and implementation best practices utilizing MS Visio. Experience troubleshooting operational systems, including participation in advanced (Tier III-level) troubleshooting and root cause analysis for deployed environments. Working knowledge of software development or scripting (e.g., XML, Python, Bash,PowerShell, Java, C++). Familiarity with system development processes, databases, and IT infrastructure concepts. Linux command line experience with certification such as CompTIA Linux+ or RHEL certification with understanding of virtual machine architecture. Must possess DoD 8140 (formerly 8570) IAT Level II certification (e.g., Security+) prior to start date and maintain certification. Willingness to travel CONUS and OCONUS up to 10% of the time. Demonstrated ability to deliver high-quality technical work, rapidly learn new technologies, and effectively collaborate with team members to support mission objectives in a dynamic environment. Ability to quickly ramp up on new systems and processes, work independently with minimal oversight, and effectively contribute in a fast-paced, evolving operational environment while maintaining high standards of quality and teamwork. Preferred Qualifications: Active TS clearance. Familiarity with containerization technologies, such as Docker and OpenShift. Cisco Certified Network Associate (CCNA) certification. Experience supporting end-to-end cross domain systems development or integration efforts. Familiarity with engineering best practices such as Raise the Bar Version 5. If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares. Original Posting:August 17, 2026 For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above. Pay Range:Pay Range $69,550.00 - $125,725.00 The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law. About Leidos Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. Headquartered in Reston, Virginia, with 47,000 global employees, Leidos reported annual revenues of approximately $16.7 billion for the fiscal year ended January 3, 2025. For more information, visit . Pay and Benefits Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available at -benefits. Securing Your Data Beware of fake employment opportunities using Leidos' name. Leidos will never ask you to provide payment-related information during any part of the employment application process (i.e., ask you for money), nor will Leidos ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Leidos will only communicate with you through emails that are generated by the automated system - never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Leidos that asks for payment-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at . If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission. Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.
Job Description Top Skills: Enterprise AI Architecture (Model gateways, Model routing, Shared AI services, Platform engineering, AI service abstraction layers, Multi-model strategies, Vendor portability, AI observability, Platform scalability, Tenant isolation, AI FinOps and cost controls) RAG and Knowledge Architecture Generative AI & Agentic AI Architecture Must have previous experience as an Architect implementing A.I into an Enterprise Environment Must have experience with Enterprise AI Platforms Enterprise Standards and Patterns Enterprise AI Strategy Target-State Architecture Architecture Governance Executive Influence Description The AI Enterprise Architect is responsible for defining, evolving, and driving the enterprise architecture for artificial intelligence across the environment. This is not a theoretical, advisory-only, or documentation-focused architecture role. The AI Enterprise Architect must be a hands-on technology leader who can move rapidly from an ambiguous business problem to an executable architecture, working reference implementation, production deployment, and measurable business outcome. The role operates at the intersection of enterprise architecture, AI engineering, data, security, cloud platforms, integration, product delivery, and business strategy. This individual will establish the enterprise direction for generative AI, agentic AI, machine learning, intelligent automation, AI-enabled applications, and shared AI platform capabilities. The AI Enterprise Architect will work within a Fortune 500 environment while supporting an AI program that operates with the urgency, experimentation, adaptability, and delivery expectations of a startup. The successful candidate must be comfortable making architecture decisions in a rapidly evolving technology landscape, challenging conventional approaches, eliminating unnecessary complexity, and personally driving initiatives through organizational and technical barriers. This individual must be capable of seeing the entire enterprise AI ecosystem while remaining close enough to implementation to validate architectures, examine code and configurations, build prototypes, identify delivery risks, and distinguish production-ready capabilities from demonstrations and vendor claims. Key Responsibilities Enterprise AI Strategy and Target Architecture • Define and maintain the enterprise AI target architecture, transition architectures, capability model, platform strategy, and multiyear architecture roadmap. • Translate business strategy and operating priorities into executable AI capabilities, architecture investments, and delivery sequences. • Establish the architectural direction for generative AI, agentic AI, machine learning, intelligent automation, AI-assisted decision-making, and AI-enabled business processes. • Define clear boundaries and relationships between enterprise AI platforms, domain solutions, shared services, data platforms, enterprise applications, and external AI providers. • Ensure that project-level AI decisions support enterprise scalability, interoperability, security, reuse, and long-term maintainability. • Identify opportunities to consolidate overlapping technologies, eliminate duplicated capabilities, and prevent uncontrolled AI platform and vendor sprawl. • Develop architecture options and recommendations that explicitly address business value, delivery speed, cost, risk, technical debt, vendor dependency, and operational complexity. • Maintain a current enterprise view of AI capabilities, platforms, models, agents, integrations, data dependencies, vendors, risks, and strategic initiatives. Hands-On AI Architecture and Delivery • Lead AI initiatives from problem definition and architecture through implementation, production deployment, adoption, and measurable outcomes. • Develop working prototypes and reference implementations to validate architecture decisions, platform capabilities, integration approaches, security controls, and delivery feasibility. • Review source code, prompts, agent definitions, tool configurations, retrieval pipelines, model configurations, APIs, infrastructure, and deployment pipelines as needed to validate solution quality. • Work directly with engineering teams to resolve architecture and implementation issues rather than limiting involvement to reviews or recommendations. • Rapidly diagnose delivery blockers, simplify overengineered approaches, reduce unnecessary scope, and establish practical paths to production. • Define production-readiness criteria and ensure that AI solutions meet requirements for reliability, security, performance, observability, supportability, cost, and business continuity. • Distinguish clearly between proof of concept, pilot, minimum viable product, production capability, and enterprise platform. • Remain personally accountable for architecture outcomes, not only architecture artifacts or review completion. Generative and Agentic AI Architecture • Design enterprise-grade architectures for large language models, multimodal models, AI assistants, autonomous and semi-autonomous agents, and AI-enabled applications. • Define patterns for single-agent and multi-agent orchestration, tool use, planning, reasoning, memory, state management, delegation, and human approval. • Establish architecture standards for retrieval-augmented generation, structured retrieval, knowledge graphs, semantic search, and enterprise knowledge access. • Define patterns for context engineering, prompt management, structured outputs, model routing, fallback, caching, and workload segmentation. • Architect secure agent access to enterprise systems, APIs, data, workflows, and external services. • Define patterns for Model Context Protocol, agent-to-agent communication, enterprise APIs, event-driven interactions, and tool integration. • Establish controls around nondeterministic model behavior, including deterministic validation, approval checkpoints, execution boundaries, and exception handling. • Evaluate when AI agents are appropriate and when conventional software, workflow automation, rules engines, APIs, or analytics provide a better solution. • Prevent the use of generative AI or agents where the architecture introduces unnecessary cost, risk, latency, or operational complexity. Enterprise AI Platform Architecture • Define the architecture for shared enterprise AI platform capabilities, including model access, model gateways, agent runtime services, retrieval services, evaluation services, security controls, observability, and cost management. • Establish reusable AI services, platform components, reference architectures, templates, development patterns, and deployment patterns. • Define enterprise model access, model selection, model portability, workload routing, quota management, and vendor abstraction strategies. • Design workload, tenant, domain, environment, and data isolation patterns appropriate to enterprise risk and operating requirements. • Establish architectural standards for proprietary, open-weight, hosted, and internally operated models. • Define integration patterns between AI platforms and enterprise cloud, data, identity, security, integration, application, and observability platforms. • Partner with platform engineering, cloud infrastructure, data, cybersecurity, and application teams to establish a scalable AI operating environment. • Ensure that platform capabilities are implemented as usable products and services rather than architecture concepts that delivery teams cannot practically adopt. Data and Knowledge Architecture • Define data and knowledge architecture required to support AI models, agents, applications, evaluation, analytics, and business processes. • Establish patterns for structured, semi-structured, and unstructured data access. • Define architectures using relational, document, graph, vector, search, streaming, and analytical technologies based on workload requirements. • Establish standards for embeddings, chunking, indexing, metadata, reranking, retrieval, source attribution, and information freshness. • Define approaches for enterprise taxonomies, ontologies, semantic models, knowledge graphs, and reusable domain knowledge. • Ensure appropriate data lineage, provenance, ownership, quality, classification, access control, retention, and usage restrictions. • Define requirements for training, fine-tuning, inference, retrieval, evaluation, monitoring, and feedback datasets. • Ensure that AI responses and actions can be traced to authoritative enterprise information where required. • Identify situations where weak data, fragmented ownership, or poor knowledge management must be corrected rather than hidden behind an AI interface. Integration and Distributed Systems Architecture • Define and enforce AI integration patterns across enterprise applications, cloud platforms, SaaS products, data platforms, APIs, workflows, and external services. • Architect synchronous and asynchronous APIs, event-driven interactions, messaging, streaming, workflow orchestration, and long-running business processes. • Establish standards for identity propagation, delegated authorization, agent identity, workload identity, and service-to-service authentication. • Define system-of-record ownership . click apply for full job details
08/23/2026
Full time
Job Description Top Skills: Enterprise AI Architecture (Model gateways, Model routing, Shared AI services, Platform engineering, AI service abstraction layers, Multi-model strategies, Vendor portability, AI observability, Platform scalability, Tenant isolation, AI FinOps and cost controls) RAG and Knowledge Architecture Generative AI & Agentic AI Architecture Must have previous experience as an Architect implementing A.I into an Enterprise Environment Must have experience with Enterprise AI Platforms Enterprise Standards and Patterns Enterprise AI Strategy Target-State Architecture Architecture Governance Executive Influence Description The AI Enterprise Architect is responsible for defining, evolving, and driving the enterprise architecture for artificial intelligence across the environment. This is not a theoretical, advisory-only, or documentation-focused architecture role. The AI Enterprise Architect must be a hands-on technology leader who can move rapidly from an ambiguous business problem to an executable architecture, working reference implementation, production deployment, and measurable business outcome. The role operates at the intersection of enterprise architecture, AI engineering, data, security, cloud platforms, integration, product delivery, and business strategy. This individual will establish the enterprise direction for generative AI, agentic AI, machine learning, intelligent automation, AI-enabled applications, and shared AI platform capabilities. The AI Enterprise Architect will work within a Fortune 500 environment while supporting an AI program that operates with the urgency, experimentation, adaptability, and delivery expectations of a startup. The successful candidate must be comfortable making architecture decisions in a rapidly evolving technology landscape, challenging conventional approaches, eliminating unnecessary complexity, and personally driving initiatives through organizational and technical barriers. This individual must be capable of seeing the entire enterprise AI ecosystem while remaining close enough to implementation to validate architectures, examine code and configurations, build prototypes, identify delivery risks, and distinguish production-ready capabilities from demonstrations and vendor claims. Key Responsibilities Enterprise AI Strategy and Target Architecture • Define and maintain the enterprise AI target architecture, transition architectures, capability model, platform strategy, and multiyear architecture roadmap. • Translate business strategy and operating priorities into executable AI capabilities, architecture investments, and delivery sequences. • Establish the architectural direction for generative AI, agentic AI, machine learning, intelligent automation, AI-assisted decision-making, and AI-enabled business processes. • Define clear boundaries and relationships between enterprise AI platforms, domain solutions, shared services, data platforms, enterprise applications, and external AI providers. • Ensure that project-level AI decisions support enterprise scalability, interoperability, security, reuse, and long-term maintainability. • Identify opportunities to consolidate overlapping technologies, eliminate duplicated capabilities, and prevent uncontrolled AI platform and vendor sprawl. • Develop architecture options and recommendations that explicitly address business value, delivery speed, cost, risk, technical debt, vendor dependency, and operational complexity. • Maintain a current enterprise view of AI capabilities, platforms, models, agents, integrations, data dependencies, vendors, risks, and strategic initiatives. Hands-On AI Architecture and Delivery • Lead AI initiatives from problem definition and architecture through implementation, production deployment, adoption, and measurable outcomes. • Develop working prototypes and reference implementations to validate architecture decisions, platform capabilities, integration approaches, security controls, and delivery feasibility. • Review source code, prompts, agent definitions, tool configurations, retrieval pipelines, model configurations, APIs, infrastructure, and deployment pipelines as needed to validate solution quality. • Work directly with engineering teams to resolve architecture and implementation issues rather than limiting involvement to reviews or recommendations. • Rapidly diagnose delivery blockers, simplify overengineered approaches, reduce unnecessary scope, and establish practical paths to production. • Define production-readiness criteria and ensure that AI solutions meet requirements for reliability, security, performance, observability, supportability, cost, and business continuity. • Distinguish clearly between proof of concept, pilot, minimum viable product, production capability, and enterprise platform. • Remain personally accountable for architecture outcomes, not only architecture artifacts or review completion. Generative and Agentic AI Architecture • Design enterprise-grade architectures for large language models, multimodal models, AI assistants, autonomous and semi-autonomous agents, and AI-enabled applications. • Define patterns for single-agent and multi-agent orchestration, tool use, planning, reasoning, memory, state management, delegation, and human approval. • Establish architecture standards for retrieval-augmented generation, structured retrieval, knowledge graphs, semantic search, and enterprise knowledge access. • Define patterns for context engineering, prompt management, structured outputs, model routing, fallback, caching, and workload segmentation. • Architect secure agent access to enterprise systems, APIs, data, workflows, and external services. • Define patterns for Model Context Protocol, agent-to-agent communication, enterprise APIs, event-driven interactions, and tool integration. • Establish controls around nondeterministic model behavior, including deterministic validation, approval checkpoints, execution boundaries, and exception handling. • Evaluate when AI agents are appropriate and when conventional software, workflow automation, rules engines, APIs, or analytics provide a better solution. • Prevent the use of generative AI or agents where the architecture introduces unnecessary cost, risk, latency, or operational complexity. Enterprise AI Platform Architecture • Define the architecture for shared enterprise AI platform capabilities, including model access, model gateways, agent runtime services, retrieval services, evaluation services, security controls, observability, and cost management. • Establish reusable AI services, platform components, reference architectures, templates, development patterns, and deployment patterns. • Define enterprise model access, model selection, model portability, workload routing, quota management, and vendor abstraction strategies. • Design workload, tenant, domain, environment, and data isolation patterns appropriate to enterprise risk and operating requirements. • Establish architectural standards for proprietary, open-weight, hosted, and internally operated models. • Define integration patterns between AI platforms and enterprise cloud, data, identity, security, integration, application, and observability platforms. • Partner with platform engineering, cloud infrastructure, data, cybersecurity, and application teams to establish a scalable AI operating environment. • Ensure that platform capabilities are implemented as usable products and services rather than architecture concepts that delivery teams cannot practically adopt. Data and Knowledge Architecture • Define data and knowledge architecture required to support AI models, agents, applications, evaluation, analytics, and business processes. • Establish patterns for structured, semi-structured, and unstructured data access. • Define architectures using relational, document, graph, vector, search, streaming, and analytical technologies based on workload requirements. • Establish standards for embeddings, chunking, indexing, metadata, reranking, retrieval, source attribution, and information freshness. • Define approaches for enterprise taxonomies, ontologies, semantic models, knowledge graphs, and reusable domain knowledge. • Ensure appropriate data lineage, provenance, ownership, quality, classification, access control, retention, and usage restrictions. • Define requirements for training, fine-tuning, inference, retrieval, evaluation, monitoring, and feedback datasets. • Ensure that AI responses and actions can be traced to authoritative enterprise information where required. • Identify situations where weak data, fragmented ownership, or poor knowledge management must be corrected rather than hidden behind an AI interface. Integration and Distributed Systems Architecture • Define and enforce AI integration patterns across enterprise applications, cloud platforms, SaaS products, data platforms, APIs, workflows, and external services. • Architect synchronous and asynchronous APIs, event-driven interactions, messaging, streaming, workflow orchestration, and long-running business processes. • Establish standards for identity propagation, delegated authorization, agent identity, workload identity, and service-to-service authentication. • Define system-of-record ownership . click apply for full job details
Job Description Top Skills' Details AI solution architecture across business, application, data, integration, security, and infrastructure domains Generative AI, RAG, and Agentic AI Architecture (Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI and multi-agent systems, Prompt engineering and context orchestration, Vector databases, embeddings, reranking, and retrieval strategies, Model selection, routing, fallback, and optimization. AI Security, Identity, and Governance Must Have previous experience as an Architect with A.I Enterprise Experience Must have For the AI Solution Architect role, Generative AI RAG / knowledge architecture Agent architecture AI evaluation AI security LLMOps / AgentOps and production operations Model architecture Enterprise integration AI economics Description The AI Solutions Architect serves as the technical architecture leader for enterprise AI solutions within the AI Center of Excellence and reports directly to the Vice President of Enterprise Architecture. This role is responsible for designing secure, scalable, supportable, and economically sustainable AI-enabled solutions from initial concept through production deployment and ongoing operation. The AI Solutions Architect owns the end-to-end architecture of assigned AI initiatives, including business requirements, artificial intelligence models, agents, data and knowledge sources, integrations, identity, security, infrastructure, observability, operational support, and governance controls. The role ensures that AI capabilities are not developed as isolated experiments, but as enterprise-grade solutions that integrate with existing business processes, platforms, applications, and data ecosystems. The architect works directly with business stakeholders, product owners, engineering teams, Cybersecurity, Data and Analytics, Infrastructure, Legal, Risk, and vendor partners while ensuring alignment with enterprise architecture strategy, standards, governance, and technology roadmaps. The individual must be able to translate business objectives into actionable architecture, validate technical designs through hands-on analysis and prototyping, and clearly communicate architectural decisions, risks, costs, and trade-offs. This position requires demonstrated experience delivering production generative AI, retrieval-augmented generation, machine learning, and agentic AI solutions. Experience limited to strategy, presentations, vendor demonstrations, or proofs of concept does not satisfy the requirements of the role. AI Solution Architecture • Design end-to-end architectures for enterprise AI solutions, including generative AI, retrieval-augmented generation, conversational AI, predictive machine learning, intelligent automation, computer vision, speech, and agentic AI capabilities. • Translate business requirements into comprehensive technical solution designs covering applications, models, agents, data, integrations, security, cloud infrastructure, observability, operations, and governance. • Determine whether artificial intelligence is appropriate for a given business problem and recommend alternative technical approaches when AI does not provide sufficient value, reliability, or economic benefit. • Define current-state, target-state, and transitional architectures for AI initiatives, including technical dependencies, shared capabilities, implementation phases, and architecture risks. • Ensure AI solutions align with enterprise architecture standards, cloud strategies, approved technology platforms, cybersecurity requirements, data governance policies, and operational support models. • Create architecture acceptance criteria and validate that proposed solutions meet functional, technical, security, operational, and business requirements before production deployment. Generative AI and Model Architecture • Design production-grade generative AI solutions using commercial, open-source, hosted, dedicated, and privately deployed models. • Evaluate and select language, vision, speech, embedding, reranking, and multimodal models based on solution quality, latency, cost, context requirements, data sensitivity, deployment options, scalability, supportability, and vendor risk. • Design multi-model architectures that support model routing, fallback, portability, workload specialization, and reduced dependency on a single model provider. • Define prompt architecture, context assembly, structured-output requirements, response validation, model fallback, caching, rate limiting, and error-handling patterns. • Evaluate model performance using repeatable technical and business criteria rather than vendor benchmarks or demonstration results alone. • Maintain awareness of model capabilities, limitations, licensing considerations, deployment constraints, and rapidly changing AI platform capabilities. Agentic AI Architecture • Design secure and reliable AI agents that can reason, use tools, maintain state, interact with enterprise applications, and execute controlled business workflows. • Define agent responsibilities, tool boundaries, memory models, workflow states, delegation rules, approval requirements, and termination conditions. • Design single-agent and multi-agent solutions using deterministic workflow controls around nondeterministic model behavior. • Establish architecture patterns for human-in-the-loop review, escalation, exception handling, retries, timeouts, circuit breakers, compensating actions, and emergency termination. • Define secure agent-to-user, agent-to-agent, and agent-to-tool interaction patterns. • Prevent uncontrolled agent autonomy by enforcing least privilege, constrained tool access, transaction limits, validation rules, and approval gates for consequential actions. • Partner with AI engineering teams to establish consistent agent development, orchestration, testing, deployment, and lifecycle management standards. Retrieval-Augmented Generation and Knowledge Architecture • Design enterprise retrieval-augmented generation solutions across structured, unstructured, document, transactional, graph, and operational data sources. • Define document ingestion, parsing, chunking, metadata enrichment, embedding, indexing, retrieval, reranking, citation, and knowledge-refresh strategies. • Design lexical, semantic, vector, hybrid, graph-enhanced, and structured retrieval patterns based on the characteristics of each use case. • Ensure retrieval solutions preserve source-system security, authorization, data classification, retention, and user entitlements. • Establish patterns for authorization-aware retrieval, source attribution, content freshness, provenance, and deletion. • Define controls for retrieval poisoning, outdated content, duplicate information, conflicting sources, inappropriate data exposure, and unsupported model responses. • Evaluate retrieval quality, answer relevance, groundedness, citation accuracy, and knowledge coverage before production deployment. AI Evaluation and Quality Engineering • Define measurable quality standards and evaluation strategies for generative AI, retrieval, machine learning, and agentic AI solutions. • Establish golden datasets, benchmark scenarios, regression suites, adversarial tests, and business acceptance criteria. • Define evaluation methods for accuracy, relevance, groundedness, hallucination, toxicity, bias, safety, retrieval quality, tool selection, tool-call accuracy, agent trajectory, and task completion. • Implement automated evaluation gates within AI development and deployment pipelines. • Define the appropriate use of human evaluation, expert review, LLM-based evaluation, deterministic testing, and statistical analysis. • Ensure model, prompt, retrieval, agent, and tool changes are tested against previous production behavior before release. • Establish production quality thresholds, monitoring requirements, rollback criteria, and exception-management processes. AI Security, Identity, and Trust Architecture • Design AI solutions in accordance with enterprise cybersecurity, privacy, identity, compliance, and risk-management requirements. • Perform AI-specific threat modeling covering prompt injection, indirect prompt injection, data poisoning, retrieval poisoning, sensitive-data exposure, model extraction, system-prompt leakage, insecure tool invocation, excessive agency, and downstream code execution. • Define identity propagation and authorization patterns across users, agents, models, tools, APIs, applications, data sources, and external services. • Design least-privilege access, workload identities, delegated authorization, service accounts, session isolation, tenant isolation, and approval controls. • Ensure agents cannot access data, tools, or transactions beyond the permissions of the requesting user or approved system identity. • Define security controls for AI gateways, model endpoints, vector stores, knowledge bases, MCP servers, external tools, plugins, third-party models, and vendor services. • Establish auditability that records who initiated an AI request, what context was used, which decisions were made, which tools were invoked, who approved an action, and what action was executed. . click apply for full job details
08/23/2026
Full time
Job Description Top Skills' Details AI solution architecture across business, application, data, integration, security, and infrastructure domains Generative AI, RAG, and Agentic AI Architecture (Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI and multi-agent systems, Prompt engineering and context orchestration, Vector databases, embeddings, reranking, and retrieval strategies, Model selection, routing, fallback, and optimization. AI Security, Identity, and Governance Must Have previous experience as an Architect with A.I Enterprise Experience Must have For the AI Solution Architect role, Generative AI RAG / knowledge architecture Agent architecture AI evaluation AI security LLMOps / AgentOps and production operations Model architecture Enterprise integration AI economics Description The AI Solutions Architect serves as the technical architecture leader for enterprise AI solutions within the AI Center of Excellence and reports directly to the Vice President of Enterprise Architecture. This role is responsible for designing secure, scalable, supportable, and economically sustainable AI-enabled solutions from initial concept through production deployment and ongoing operation. The AI Solutions Architect owns the end-to-end architecture of assigned AI initiatives, including business requirements, artificial intelligence models, agents, data and knowledge sources, integrations, identity, security, infrastructure, observability, operational support, and governance controls. The role ensures that AI capabilities are not developed as isolated experiments, but as enterprise-grade solutions that integrate with existing business processes, platforms, applications, and data ecosystems. The architect works directly with business stakeholders, product owners, engineering teams, Cybersecurity, Data and Analytics, Infrastructure, Legal, Risk, and vendor partners while ensuring alignment with enterprise architecture strategy, standards, governance, and technology roadmaps. The individual must be able to translate business objectives into actionable architecture, validate technical designs through hands-on analysis and prototyping, and clearly communicate architectural decisions, risks, costs, and trade-offs. This position requires demonstrated experience delivering production generative AI, retrieval-augmented generation, machine learning, and agentic AI solutions. Experience limited to strategy, presentations, vendor demonstrations, or proofs of concept does not satisfy the requirements of the role. AI Solution Architecture • Design end-to-end architectures for enterprise AI solutions, including generative AI, retrieval-augmented generation, conversational AI, predictive machine learning, intelligent automation, computer vision, speech, and agentic AI capabilities. • Translate business requirements into comprehensive technical solution designs covering applications, models, agents, data, integrations, security, cloud infrastructure, observability, operations, and governance. • Determine whether artificial intelligence is appropriate for a given business problem and recommend alternative technical approaches when AI does not provide sufficient value, reliability, or economic benefit. • Define current-state, target-state, and transitional architectures for AI initiatives, including technical dependencies, shared capabilities, implementation phases, and architecture risks. • Ensure AI solutions align with enterprise architecture standards, cloud strategies, approved technology platforms, cybersecurity requirements, data governance policies, and operational support models. • Create architecture acceptance criteria and validate that proposed solutions meet functional, technical, security, operational, and business requirements before production deployment. Generative AI and Model Architecture • Design production-grade generative AI solutions using commercial, open-source, hosted, dedicated, and privately deployed models. • Evaluate and select language, vision, speech, embedding, reranking, and multimodal models based on solution quality, latency, cost, context requirements, data sensitivity, deployment options, scalability, supportability, and vendor risk. • Design multi-model architectures that support model routing, fallback, portability, workload specialization, and reduced dependency on a single model provider. • Define prompt architecture, context assembly, structured-output requirements, response validation, model fallback, caching, rate limiting, and error-handling patterns. • Evaluate model performance using repeatable technical and business criteria rather than vendor benchmarks or demonstration results alone. • Maintain awareness of model capabilities, limitations, licensing considerations, deployment constraints, and rapidly changing AI platform capabilities. Agentic AI Architecture • Design secure and reliable AI agents that can reason, use tools, maintain state, interact with enterprise applications, and execute controlled business workflows. • Define agent responsibilities, tool boundaries, memory models, workflow states, delegation rules, approval requirements, and termination conditions. • Design single-agent and multi-agent solutions using deterministic workflow controls around nondeterministic model behavior. • Establish architecture patterns for human-in-the-loop review, escalation, exception handling, retries, timeouts, circuit breakers, compensating actions, and emergency termination. • Define secure agent-to-user, agent-to-agent, and agent-to-tool interaction patterns. • Prevent uncontrolled agent autonomy by enforcing least privilege, constrained tool access, transaction limits, validation rules, and approval gates for consequential actions. • Partner with AI engineering teams to establish consistent agent development, orchestration, testing, deployment, and lifecycle management standards. Retrieval-Augmented Generation and Knowledge Architecture • Design enterprise retrieval-augmented generation solutions across structured, unstructured, document, transactional, graph, and operational data sources. • Define document ingestion, parsing, chunking, metadata enrichment, embedding, indexing, retrieval, reranking, citation, and knowledge-refresh strategies. • Design lexical, semantic, vector, hybrid, graph-enhanced, and structured retrieval patterns based on the characteristics of each use case. • Ensure retrieval solutions preserve source-system security, authorization, data classification, retention, and user entitlements. • Establish patterns for authorization-aware retrieval, source attribution, content freshness, provenance, and deletion. • Define controls for retrieval poisoning, outdated content, duplicate information, conflicting sources, inappropriate data exposure, and unsupported model responses. • Evaluate retrieval quality, answer relevance, groundedness, citation accuracy, and knowledge coverage before production deployment. AI Evaluation and Quality Engineering • Define measurable quality standards and evaluation strategies for generative AI, retrieval, machine learning, and agentic AI solutions. • Establish golden datasets, benchmark scenarios, regression suites, adversarial tests, and business acceptance criteria. • Define evaluation methods for accuracy, relevance, groundedness, hallucination, toxicity, bias, safety, retrieval quality, tool selection, tool-call accuracy, agent trajectory, and task completion. • Implement automated evaluation gates within AI development and deployment pipelines. • Define the appropriate use of human evaluation, expert review, LLM-based evaluation, deterministic testing, and statistical analysis. • Ensure model, prompt, retrieval, agent, and tool changes are tested against previous production behavior before release. • Establish production quality thresholds, monitoring requirements, rollback criteria, and exception-management processes. AI Security, Identity, and Trust Architecture • Design AI solutions in accordance with enterprise cybersecurity, privacy, identity, compliance, and risk-management requirements. • Perform AI-specific threat modeling covering prompt injection, indirect prompt injection, data poisoning, retrieval poisoning, sensitive-data exposure, model extraction, system-prompt leakage, insecure tool invocation, excessive agency, and downstream code execution. • Define identity propagation and authorization patterns across users, agents, models, tools, APIs, applications, data sources, and external services. • Design least-privilege access, workload identities, delegated authorization, service accounts, session isolation, tenant isolation, and approval controls. • Ensure agents cannot access data, tools, or transactions beyond the permissions of the requesting user or approved system identity. • Define security controls for AI gateways, model endpoints, vector stores, knowledge bases, MCP servers, external tools, plugins, third-party models, and vendor services. • Establish auditability that records who initiated an AI request, what context was used, which decisions were made, which tools were invoked, who approved an action, and what action was executed. . click apply for full job details
Job Description Top Skills' Details AI solution architecture across business, application, data, integration, security, and infrastructure domains Generative AI, RAG, and Agentic AI Architecture (Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI and multi-agent systems, Prompt engineering and context orchestration, Vector databases, embeddings, reranking, and retrieval strategies, Model selection, routing, fallback, and optimization. AI Security, Identity, and Governance Must Have previous experience as an Architect with A.I Enterprise Experience Must have For the AI Solution Architect role, Generative AI RAG / knowledge architecture Agent architecture AI evaluation AI security LLMOps / AgentOps and production operations Model architecture Enterprise integration AI economics Description The AI Solutions Architect serves as the technical architecture leader for enterprise AI solutions within the AI Center of Excellence and reports directly to the Vice President of Enterprise Architecture. This role is responsible for designing secure, scalable, supportable, and economically sustainable AI-enabled solutions from initial concept through production deployment and ongoing operation. The AI Solutions Architect owns the end-to-end architecture of assigned AI initiatives, including business requirements, artificial intelligence models, agents, data and knowledge sources, integrations, identity, security, infrastructure, observability, operational support, and governance controls. The role ensures that AI capabilities are not developed as isolated experiments, but as enterprise-grade solutions that integrate with existing business processes, platforms, applications, and data ecosystems. The architect works directly with business stakeholders, product owners, engineering teams, Cybersecurity, Data and Analytics, Infrastructure, Legal, Risk, and vendor partners while ensuring alignment with enterprise architecture strategy, standards, governance, and technology roadmaps. The individual must be able to translate business objectives into actionable architecture, validate technical designs through hands-on analysis and prototyping, and clearly communicate architectural decisions, risks, costs, and trade-offs. This position requires demonstrated experience delivering production generative AI, retrieval-augmented generation, machine learning, and agentic AI solutions. Experience limited to strategy, presentations, vendor demonstrations, or proofs of concept does not satisfy the requirements of the role. AI Solution Architecture • Design end-to-end architectures for enterprise AI solutions, including generative AI, retrieval-augmented generation, conversational AI, predictive machine learning, intelligent automation, computer vision, speech, and agentic AI capabilities. • Translate business requirements into comprehensive technical solution designs covering applications, models, agents, data, integrations, security, cloud infrastructure, observability, operations, and governance. • Determine whether artificial intelligence is appropriate for a given business problem and recommend alternative technical approaches when AI does not provide sufficient value, reliability, or economic benefit. • Define current-state, target-state, and transitional architectures for AI initiatives, including technical dependencies, shared capabilities, implementation phases, and architecture risks. • Ensure AI solutions align with enterprise architecture standards, cloud strategies, approved technology platforms, cybersecurity requirements, data governance policies, and operational support models. • Create architecture acceptance criteria and validate that proposed solutions meet functional, technical, security, operational, and business requirements before production deployment. Generative AI and Model Architecture • Design production-grade generative AI solutions using commercial, open-source, hosted, dedicated, and privately deployed models. • Evaluate and select language, vision, speech, embedding, reranking, and multimodal models based on solution quality, latency, cost, context requirements, data sensitivity, deployment options, scalability, supportability, and vendor risk. • Design multi-model architectures that support model routing, fallback, portability, workload specialization, and reduced dependency on a single model provider. • Define prompt architecture, context assembly, structured-output requirements, response validation, model fallback, caching, rate limiting, and error-handling patterns. • Evaluate model performance using repeatable technical and business criteria rather than vendor benchmarks or demonstration results alone. • Maintain awareness of model capabilities, limitations, licensing considerations, deployment constraints, and rapidly changing AI platform capabilities. Agentic AI Architecture • Design secure and reliable AI agents that can reason, use tools, maintain state, interact with enterprise applications, and execute controlled business workflows. • Define agent responsibilities, tool boundaries, memory models, workflow states, delegation rules, approval requirements, and termination conditions. • Design single-agent and multi-agent solutions using deterministic workflow controls around nondeterministic model behavior. • Establish architecture patterns for human-in-the-loop review, escalation, exception handling, retries, timeouts, circuit breakers, compensating actions, and emergency termination. • Define secure agent-to-user, agent-to-agent, and agent-to-tool interaction patterns. • Prevent uncontrolled agent autonomy by enforcing least privilege, constrained tool access, transaction limits, validation rules, and approval gates for consequential actions. • Partner with AI engineering teams to establish consistent agent development, orchestration, testing, deployment, and lifecycle management standards. Retrieval-Augmented Generation and Knowledge Architecture • Design enterprise retrieval-augmented generation solutions across structured, unstructured, document, transactional, graph, and operational data sources. • Define document ingestion, parsing, chunking, metadata enrichment, embedding, indexing, retrieval, reranking, citation, and knowledge-refresh strategies. • Design lexical, semantic, vector, hybrid, graph-enhanced, and structured retrieval patterns based on the characteristics of each use case. • Ensure retrieval solutions preserve source-system security, authorization, data classification, retention, and user entitlements. • Establish patterns for authorization-aware retrieval, source attribution, content freshness, provenance, and deletion. • Define controls for retrieval poisoning, outdated content, duplicate information, conflicting sources, inappropriate data exposure, and unsupported model responses. • Evaluate retrieval quality, answer relevance, groundedness, citation accuracy, and knowledge coverage before production deployment. AI Evaluation and Quality Engineering • Define measurable quality standards and evaluation strategies for generative AI, retrieval, machine learning, and agentic AI solutions. • Establish golden datasets, benchmark scenarios, regression suites, adversarial tests, and business acceptance criteria. • Define evaluation methods for accuracy, relevance, groundedness, hallucination, toxicity, bias, safety, retrieval quality, tool selection, tool-call accuracy, agent trajectory, and task completion. • Implement automated evaluation gates within AI development and deployment pipelines. • Define the appropriate use of human evaluation, expert review, LLM-based evaluation, deterministic testing, and statistical analysis. • Ensure model, prompt, retrieval, agent, and tool changes are tested against previous production behavior before release. • Establish production quality thresholds, monitoring requirements, rollback criteria, and exception-management processes. AI Security, Identity, and Trust Architecture • Design AI solutions in accordance with enterprise cybersecurity, privacy, identity, compliance, and risk-management requirements. • Perform AI-specific threat modeling covering prompt injection, indirect prompt injection, data poisoning, retrieval poisoning, sensitive-data exposure, model extraction, system-prompt leakage, insecure tool invocation, excessive agency, and downstream code execution. • Define identity propagation and authorization patterns across users, agents, models, tools, APIs, applications, data sources, and external services. • Design least-privilege access, workload identities, delegated authorization, service accounts, session isolation, tenant isolation, and approval controls. • Ensure agents cannot access data, tools, or transactions beyond the permissions of the requesting user or approved system identity. • Define security controls for AI gateways, model endpoints, vector stores, knowledge bases, MCP servers, external tools, plugins, third-party models, and vendor services. • Establish auditability that records who initiated an AI request, what context was used, which decisions were made, which tools were invoked, who approved an action, and what action was executed. . click apply for full job details
08/23/2026
Full time
Job Description Top Skills' Details AI solution architecture across business, application, data, integration, security, and infrastructure domains Generative AI, RAG, and Agentic AI Architecture (Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI and multi-agent systems, Prompt engineering and context orchestration, Vector databases, embeddings, reranking, and retrieval strategies, Model selection, routing, fallback, and optimization. AI Security, Identity, and Governance Must Have previous experience as an Architect with A.I Enterprise Experience Must have For the AI Solution Architect role, Generative AI RAG / knowledge architecture Agent architecture AI evaluation AI security LLMOps / AgentOps and production operations Model architecture Enterprise integration AI economics Description The AI Solutions Architect serves as the technical architecture leader for enterprise AI solutions within the AI Center of Excellence and reports directly to the Vice President of Enterprise Architecture. This role is responsible for designing secure, scalable, supportable, and economically sustainable AI-enabled solutions from initial concept through production deployment and ongoing operation. The AI Solutions Architect owns the end-to-end architecture of assigned AI initiatives, including business requirements, artificial intelligence models, agents, data and knowledge sources, integrations, identity, security, infrastructure, observability, operational support, and governance controls. The role ensures that AI capabilities are not developed as isolated experiments, but as enterprise-grade solutions that integrate with existing business processes, platforms, applications, and data ecosystems. The architect works directly with business stakeholders, product owners, engineering teams, Cybersecurity, Data and Analytics, Infrastructure, Legal, Risk, and vendor partners while ensuring alignment with enterprise architecture strategy, standards, governance, and technology roadmaps. The individual must be able to translate business objectives into actionable architecture, validate technical designs through hands-on analysis and prototyping, and clearly communicate architectural decisions, risks, costs, and trade-offs. This position requires demonstrated experience delivering production generative AI, retrieval-augmented generation, machine learning, and agentic AI solutions. Experience limited to strategy, presentations, vendor demonstrations, or proofs of concept does not satisfy the requirements of the role. AI Solution Architecture • Design end-to-end architectures for enterprise AI solutions, including generative AI, retrieval-augmented generation, conversational AI, predictive machine learning, intelligent automation, computer vision, speech, and agentic AI capabilities. • Translate business requirements into comprehensive technical solution designs covering applications, models, agents, data, integrations, security, cloud infrastructure, observability, operations, and governance. • Determine whether artificial intelligence is appropriate for a given business problem and recommend alternative technical approaches when AI does not provide sufficient value, reliability, or economic benefit. • Define current-state, target-state, and transitional architectures for AI initiatives, including technical dependencies, shared capabilities, implementation phases, and architecture risks. • Ensure AI solutions align with enterprise architecture standards, cloud strategies, approved technology platforms, cybersecurity requirements, data governance policies, and operational support models. • Create architecture acceptance criteria and validate that proposed solutions meet functional, technical, security, operational, and business requirements before production deployment. Generative AI and Model Architecture • Design production-grade generative AI solutions using commercial, open-source, hosted, dedicated, and privately deployed models. • Evaluate and select language, vision, speech, embedding, reranking, and multimodal models based on solution quality, latency, cost, context requirements, data sensitivity, deployment options, scalability, supportability, and vendor risk. • Design multi-model architectures that support model routing, fallback, portability, workload specialization, and reduced dependency on a single model provider. • Define prompt architecture, context assembly, structured-output requirements, response validation, model fallback, caching, rate limiting, and error-handling patterns. • Evaluate model performance using repeatable technical and business criteria rather than vendor benchmarks or demonstration results alone. • Maintain awareness of model capabilities, limitations, licensing considerations, deployment constraints, and rapidly changing AI platform capabilities. Agentic AI Architecture • Design secure and reliable AI agents that can reason, use tools, maintain state, interact with enterprise applications, and execute controlled business workflows. • Define agent responsibilities, tool boundaries, memory models, workflow states, delegation rules, approval requirements, and termination conditions. • Design single-agent and multi-agent solutions using deterministic workflow controls around nondeterministic model behavior. • Establish architecture patterns for human-in-the-loop review, escalation, exception handling, retries, timeouts, circuit breakers, compensating actions, and emergency termination. • Define secure agent-to-user, agent-to-agent, and agent-to-tool interaction patterns. • Prevent uncontrolled agent autonomy by enforcing least privilege, constrained tool access, transaction limits, validation rules, and approval gates for consequential actions. • Partner with AI engineering teams to establish consistent agent development, orchestration, testing, deployment, and lifecycle management standards. Retrieval-Augmented Generation and Knowledge Architecture • Design enterprise retrieval-augmented generation solutions across structured, unstructured, document, transactional, graph, and operational data sources. • Define document ingestion, parsing, chunking, metadata enrichment, embedding, indexing, retrieval, reranking, citation, and knowledge-refresh strategies. • Design lexical, semantic, vector, hybrid, graph-enhanced, and structured retrieval patterns based on the characteristics of each use case. • Ensure retrieval solutions preserve source-system security, authorization, data classification, retention, and user entitlements. • Establish patterns for authorization-aware retrieval, source attribution, content freshness, provenance, and deletion. • Define controls for retrieval poisoning, outdated content, duplicate information, conflicting sources, inappropriate data exposure, and unsupported model responses. • Evaluate retrieval quality, answer relevance, groundedness, citation accuracy, and knowledge coverage before production deployment. AI Evaluation and Quality Engineering • Define measurable quality standards and evaluation strategies for generative AI, retrieval, machine learning, and agentic AI solutions. • Establish golden datasets, benchmark scenarios, regression suites, adversarial tests, and business acceptance criteria. • Define evaluation methods for accuracy, relevance, groundedness, hallucination, toxicity, bias, safety, retrieval quality, tool selection, tool-call accuracy, agent trajectory, and task completion. • Implement automated evaluation gates within AI development and deployment pipelines. • Define the appropriate use of human evaluation, expert review, LLM-based evaluation, deterministic testing, and statistical analysis. • Ensure model, prompt, retrieval, agent, and tool changes are tested against previous production behavior before release. • Establish production quality thresholds, monitoring requirements, rollback criteria, and exception-management processes. AI Security, Identity, and Trust Architecture • Design AI solutions in accordance with enterprise cybersecurity, privacy, identity, compliance, and risk-management requirements. • Perform AI-specific threat modeling covering prompt injection, indirect prompt injection, data poisoning, retrieval poisoning, sensitive-data exposure, model extraction, system-prompt leakage, insecure tool invocation, excessive agency, and downstream code execution. • Define identity propagation and authorization patterns across users, agents, models, tools, APIs, applications, data sources, and external services. • Design least-privilege access, workload identities, delegated authorization, service accounts, session isolation, tenant isolation, and approval controls. • Ensure agents cannot access data, tools, or transactions beyond the permissions of the requesting user or approved system identity. • Define security controls for AI gateways, model endpoints, vector stores, knowledge bases, MCP servers, external tools, plugins, third-party models, and vendor services. • Establish auditability that records who initiated an AI request, what context was used, which decisions were made, which tools were invoked, who approved an action, and what action was executed. . click apply for full job details
Job Description Top Skills: Enterprise AI Architecture (Model gateways, Model routing, Shared AI services, Platform engineering, AI service abstraction layers, Multi-model strategies, Vendor portability, AI observability, Platform scalability, Tenant isolation, AI FinOps and cost controls) RAG and Knowledge Architecture Generative AI & Agentic AI Architecture Must have previous experience as an Architect implementing A.I into an Enterprise Environment Must have experience with Enterprise AI Platforms Enterprise Standards and Patterns Enterprise AI Strategy Target-State Architecture Architecture Governance Executive Influence Description The AI Enterprise Architect is responsible for defining, evolving, and driving the enterprise architecture for artificial intelligence across the environment. This is not a theoretical, advisory-only, or documentation-focused architecture role. The AI Enterprise Architect must be a hands-on technology leader who can move rapidly from an ambiguous business problem to an executable architecture, working reference implementation, production deployment, and measurable business outcome. The role operates at the intersection of enterprise architecture, AI engineering, data, security, cloud platforms, integration, product delivery, and business strategy. This individual will establish the enterprise direction for generative AI, agentic AI, machine learning, intelligent automation, AI-enabled applications, and shared AI platform capabilities. The AI Enterprise Architect will work within a Fortune 500 environment while supporting an AI program that operates with the urgency, experimentation, adaptability, and delivery expectations of a startup. The successful candidate must be comfortable making architecture decisions in a rapidly evolving technology landscape, challenging conventional approaches, eliminating unnecessary complexity, and personally driving initiatives through organizational and technical barriers. This individual must be capable of seeing the entire enterprise AI ecosystem while remaining close enough to implementation to validate architectures, examine code and configurations, build prototypes, identify delivery risks, and distinguish production-ready capabilities from demonstrations and vendor claims. Key Responsibilities Enterprise AI Strategy and Target Architecture • Define and maintain the enterprise AI target architecture, transition architectures, capability model, platform strategy, and multiyear architecture roadmap. • Translate business strategy and operating priorities into executable AI capabilities, architecture investments, and delivery sequences. • Establish the architectural direction for generative AI, agentic AI, machine learning, intelligent automation, AI-assisted decision-making, and AI-enabled business processes. • Define clear boundaries and relationships between enterprise AI platforms, domain solutions, shared services, data platforms, enterprise applications, and external AI providers. • Ensure that project-level AI decisions support enterprise scalability, interoperability, security, reuse, and long-term maintainability. • Identify opportunities to consolidate overlapping technologies, eliminate duplicated capabilities, and prevent uncontrolled AI platform and vendor sprawl. • Develop architecture options and recommendations that explicitly address business value, delivery speed, cost, risk, technical debt, vendor dependency, and operational complexity. • Maintain a current enterprise view of AI capabilities, platforms, models, agents, integrations, data dependencies, vendors, risks, and strategic initiatives. Hands-On AI Architecture and Delivery • Lead AI initiatives from problem definition and architecture through implementation, production deployment, adoption, and measurable outcomes. • Develop working prototypes and reference implementations to validate architecture decisions, platform capabilities, integration approaches, security controls, and delivery feasibility. • Review source code, prompts, agent definitions, tool configurations, retrieval pipelines, model configurations, APIs, infrastructure, and deployment pipelines as needed to validate solution quality. • Work directly with engineering teams to resolve architecture and implementation issues rather than limiting involvement to reviews or recommendations. • Rapidly diagnose delivery blockers, simplify overengineered approaches, reduce unnecessary scope, and establish practical paths to production. • Define production-readiness criteria and ensure that AI solutions meet requirements for reliability, security, performance, observability, supportability, cost, and business continuity. • Distinguish clearly between proof of concept, pilot, minimum viable product, production capability, and enterprise platform. • Remain personally accountable for architecture outcomes, not only architecture artifacts or review completion. Generative and Agentic AI Architecture • Design enterprise-grade architectures for large language models, multimodal models, AI assistants, autonomous and semi-autonomous agents, and AI-enabled applications. • Define patterns for single-agent and multi-agent orchestration, tool use, planning, reasoning, memory, state management, delegation, and human approval. • Establish architecture standards for retrieval-augmented generation, structured retrieval, knowledge graphs, semantic search, and enterprise knowledge access. • Define patterns for context engineering, prompt management, structured outputs, model routing, fallback, caching, and workload segmentation. • Architect secure agent access to enterprise systems, APIs, data, workflows, and external services. • Define patterns for Model Context Protocol, agent-to-agent communication, enterprise APIs, event-driven interactions, and tool integration. • Establish controls around nondeterministic model behavior, including deterministic validation, approval checkpoints, execution boundaries, and exception handling. • Evaluate when AI agents are appropriate and when conventional software, workflow automation, rules engines, APIs, or analytics provide a better solution. • Prevent the use of generative AI or agents where the architecture introduces unnecessary cost, risk, latency, or operational complexity. Enterprise AI Platform Architecture • Define the architecture for shared enterprise AI platform capabilities, including model access, model gateways, agent runtime services, retrieval services, evaluation services, security controls, observability, and cost management. • Establish reusable AI services, platform components, reference architectures, templates, development patterns, and deployment patterns. • Define enterprise model access, model selection, model portability, workload routing, quota management, and vendor abstraction strategies. • Design workload, tenant, domain, environment, and data isolation patterns appropriate to enterprise risk and operating requirements. • Establish architectural standards for proprietary, open-weight, hosted, and internally operated models. • Define integration patterns between AI platforms and enterprise cloud, data, identity, security, integration, application, and observability platforms. • Partner with platform engineering, cloud infrastructure, data, cybersecurity, and application teams to establish a scalable AI operating environment. • Ensure that platform capabilities are implemented as usable products and services rather than architecture concepts that delivery teams cannot practically adopt. Data and Knowledge Architecture • Define data and knowledge architecture required to support AI models, agents, applications, evaluation, analytics, and business processes. • Establish patterns for structured, semi-structured, and unstructured data access. • Define architectures using relational, document, graph, vector, search, streaming, and analytical technologies based on workload requirements. • Establish standards for embeddings, chunking, indexing, metadata, reranking, retrieval, source attribution, and information freshness. • Define approaches for enterprise taxonomies, ontologies, semantic models, knowledge graphs, and reusable domain knowledge. • Ensure appropriate data lineage, provenance, ownership, quality, classification, access control, retention, and usage restrictions. • Define requirements for training, fine-tuning, inference, retrieval, evaluation, monitoring, and feedback datasets. • Ensure that AI responses and actions can be traced to authoritative enterprise information where required. • Identify situations where weak data, fragmented ownership, or poor knowledge management must be corrected rather than hidden behind an AI interface. Integration and Distributed Systems Architecture • Define and enforce AI integration patterns across enterprise applications, cloud platforms, SaaS products, data platforms, APIs, workflows, and external services. • Architect synchronous and asynchronous APIs, event-driven interactions, messaging, streaming, workflow orchestration, and long-running business processes. • Establish standards for identity propagation, delegated authorization, agent identity, workload identity, and service-to-service authentication. • Define system-of-record ownership . click apply for full job details
08/23/2026
Full time
Job Description Top Skills: Enterprise AI Architecture (Model gateways, Model routing, Shared AI services, Platform engineering, AI service abstraction layers, Multi-model strategies, Vendor portability, AI observability, Platform scalability, Tenant isolation, AI FinOps and cost controls) RAG and Knowledge Architecture Generative AI & Agentic AI Architecture Must have previous experience as an Architect implementing A.I into an Enterprise Environment Must have experience with Enterprise AI Platforms Enterprise Standards and Patterns Enterprise AI Strategy Target-State Architecture Architecture Governance Executive Influence Description The AI Enterprise Architect is responsible for defining, evolving, and driving the enterprise architecture for artificial intelligence across the environment. This is not a theoretical, advisory-only, or documentation-focused architecture role. The AI Enterprise Architect must be a hands-on technology leader who can move rapidly from an ambiguous business problem to an executable architecture, working reference implementation, production deployment, and measurable business outcome. The role operates at the intersection of enterprise architecture, AI engineering, data, security, cloud platforms, integration, product delivery, and business strategy. This individual will establish the enterprise direction for generative AI, agentic AI, machine learning, intelligent automation, AI-enabled applications, and shared AI platform capabilities. The AI Enterprise Architect will work within a Fortune 500 environment while supporting an AI program that operates with the urgency, experimentation, adaptability, and delivery expectations of a startup. The successful candidate must be comfortable making architecture decisions in a rapidly evolving technology landscape, challenging conventional approaches, eliminating unnecessary complexity, and personally driving initiatives through organizational and technical barriers. This individual must be capable of seeing the entire enterprise AI ecosystem while remaining close enough to implementation to validate architectures, examine code and configurations, build prototypes, identify delivery risks, and distinguish production-ready capabilities from demonstrations and vendor claims. Key Responsibilities Enterprise AI Strategy and Target Architecture • Define and maintain the enterprise AI target architecture, transition architectures, capability model, platform strategy, and multiyear architecture roadmap. • Translate business strategy and operating priorities into executable AI capabilities, architecture investments, and delivery sequences. • Establish the architectural direction for generative AI, agentic AI, machine learning, intelligent automation, AI-assisted decision-making, and AI-enabled business processes. • Define clear boundaries and relationships between enterprise AI platforms, domain solutions, shared services, data platforms, enterprise applications, and external AI providers. • Ensure that project-level AI decisions support enterprise scalability, interoperability, security, reuse, and long-term maintainability. • Identify opportunities to consolidate overlapping technologies, eliminate duplicated capabilities, and prevent uncontrolled AI platform and vendor sprawl. • Develop architecture options and recommendations that explicitly address business value, delivery speed, cost, risk, technical debt, vendor dependency, and operational complexity. • Maintain a current enterprise view of AI capabilities, platforms, models, agents, integrations, data dependencies, vendors, risks, and strategic initiatives. Hands-On AI Architecture and Delivery • Lead AI initiatives from problem definition and architecture through implementation, production deployment, adoption, and measurable outcomes. • Develop working prototypes and reference implementations to validate architecture decisions, platform capabilities, integration approaches, security controls, and delivery feasibility. • Review source code, prompts, agent definitions, tool configurations, retrieval pipelines, model configurations, APIs, infrastructure, and deployment pipelines as needed to validate solution quality. • Work directly with engineering teams to resolve architecture and implementation issues rather than limiting involvement to reviews or recommendations. • Rapidly diagnose delivery blockers, simplify overengineered approaches, reduce unnecessary scope, and establish practical paths to production. • Define production-readiness criteria and ensure that AI solutions meet requirements for reliability, security, performance, observability, supportability, cost, and business continuity. • Distinguish clearly between proof of concept, pilot, minimum viable product, production capability, and enterprise platform. • Remain personally accountable for architecture outcomes, not only architecture artifacts or review completion. Generative and Agentic AI Architecture • Design enterprise-grade architectures for large language models, multimodal models, AI assistants, autonomous and semi-autonomous agents, and AI-enabled applications. • Define patterns for single-agent and multi-agent orchestration, tool use, planning, reasoning, memory, state management, delegation, and human approval. • Establish architecture standards for retrieval-augmented generation, structured retrieval, knowledge graphs, semantic search, and enterprise knowledge access. • Define patterns for context engineering, prompt management, structured outputs, model routing, fallback, caching, and workload segmentation. • Architect secure agent access to enterprise systems, APIs, data, workflows, and external services. • Define patterns for Model Context Protocol, agent-to-agent communication, enterprise APIs, event-driven interactions, and tool integration. • Establish controls around nondeterministic model behavior, including deterministic validation, approval checkpoints, execution boundaries, and exception handling. • Evaluate when AI agents are appropriate and when conventional software, workflow automation, rules engines, APIs, or analytics provide a better solution. • Prevent the use of generative AI or agents where the architecture introduces unnecessary cost, risk, latency, or operational complexity. Enterprise AI Platform Architecture • Define the architecture for shared enterprise AI platform capabilities, including model access, model gateways, agent runtime services, retrieval services, evaluation services, security controls, observability, and cost management. • Establish reusable AI services, platform components, reference architectures, templates, development patterns, and deployment patterns. • Define enterprise model access, model selection, model portability, workload routing, quota management, and vendor abstraction strategies. • Design workload, tenant, domain, environment, and data isolation patterns appropriate to enterprise risk and operating requirements. • Establish architectural standards for proprietary, open-weight, hosted, and internally operated models. • Define integration patterns between AI platforms and enterprise cloud, data, identity, security, integration, application, and observability platforms. • Partner with platform engineering, cloud infrastructure, data, cybersecurity, and application teams to establish a scalable AI operating environment. • Ensure that platform capabilities are implemented as usable products and services rather than architecture concepts that delivery teams cannot practically adopt. Data and Knowledge Architecture • Define data and knowledge architecture required to support AI models, agents, applications, evaluation, analytics, and business processes. • Establish patterns for structured, semi-structured, and unstructured data access. • Define architectures using relational, document, graph, vector, search, streaming, and analytical technologies based on workload requirements. • Establish standards for embeddings, chunking, indexing, metadata, reranking, retrieval, source attribution, and information freshness. • Define approaches for enterprise taxonomies, ontologies, semantic models, knowledge graphs, and reusable domain knowledge. • Ensure appropriate data lineage, provenance, ownership, quality, classification, access control, retention, and usage restrictions. • Define requirements for training, fine-tuning, inference, retrieval, evaluation, monitoring, and feedback datasets. • Ensure that AI responses and actions can be traced to authoritative enterprise information where required. • Identify situations where weak data, fragmented ownership, or poor knowledge management must be corrected rather than hidden behind an AI interface. Integration and Distributed Systems Architecture • Define and enforce AI integration patterns across enterprise applications, cloud platforms, SaaS products, data platforms, APIs, workflows, and external services. • Architect synchronous and asynchronous APIs, event-driven interactions, messaging, streaming, workflow orchestration, and long-running business processes. • Establish standards for identity propagation, delegated authorization, agent identity, workload identity, and service-to-service authentication. • Define system-of-record ownership . click apply for full job details
Job Description Summary As a GE Vernova accelerator, GE Vernova Advanced Research is driving strategy and leading research & development efforts to execute on the business's mission to power the energy transition. We forge the partnerships and invent the technologies required to electrify and decarbonize for a zero-carbon future. Representing every major scientific and engineering discipline, our researchers are collaborating with GE Vernova's businesses, the U.S. government, and more than 420 technology partners to execute on 150+ energy-focused projects. Collectively, these research programs and initiatives aim to solve near term technical challenges, deliver next generation product advances, and drive long term breakthrough innovation to enable more affordable, reliable, sustainable, and secure energy. Job Description As Control Systems Research Engineer, you will enable Energy Transition through the development of models, advanced controls, optimization, estimation, and detection technologies. You will contribute to R&D programs aligned with GE Vernova's energy businesses such as Renewable Energy, Gas Power, Grid Solutions, and Power Conversion as well as with U.S. Government Agencies such as the Department of Energy and the Department of Defense. The technologies developed under these programs will be matured and transitioned into GE business products where they drive breakthrough impact for our company, our customers, and our society. As a Control Systems Research Engineer, you will: Work independently as well as in diverse teams to develop and apply advanced technology solutions to GE Vernova products and services using modeling, advanced controls, optimization, estimation, and detection technologies. Validate performance of developed solutions through simulations and application on target systems. Document technology and results through patent applications, technical reports, and publications. Stay current with advances in system technologies to seek out new ideas and applications. Work in a team environment with colleagues across GE Research, GE Vernova, and partners from industry, academia, and government agencies. Position Requirements Doctorate degree in an Engineering or related field with experience in design of advanced controls, optimization, estimation, or detection algorithms or a Master's degree in an Engineering or related field with a minimum of 3 years of experience in design of advanced controls, optimization, estimation, or detection algorithms. 3+ years of knowledge and application of advanced controls, optimization, estimation, or detection algorithms. 3+ years of proficiency in MATLAB/Simulink and C, C++, or Python. 3+ years of demonstrated experience (algorithm development and software implementation) in developing systems solutions for complex physical systems (e.g., mechanical systems, chemical plants, power systems, transportation, aviation systems). Desired Qualifications Active/current U.S. security clearance Experience with machine learning and artificial intelligence techniques and the application of those in a controls context Experience with real-time implementation of system solutions in a variety of hardware platforms Experience developing controls solutions for industrial applications Experience with machine learning and artificial intelligence techniques and the application of those in a controls context Experience developing cyber-security algorithms Exposure to industrial control hardware programming and industrial control software development Proficiency in source code management through git/github Experience with application for government funding and project proposal writing Experience with program management, which includes setting up schedules, milestones, technical reviews, tracking funding, etc. Demonstrated ability to take an innovative idea from a concept to a product Eligibility Requirements Legal authorization to work in the U.S. is required. We will not sponsor individuals at the Masters level for employment visas, now or in the future, for this job opening. Must be willing to work out of an office located in Niskayuna, NY. Ability to maintain a U.S. security clearance, prerequisite for clearance is U.S. citizenship. GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation. GE Vernova is an Equal Opportunity Employer . Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law. GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable). Relocation Assistance Provided: Yes For candidates applying to a U.S. based position, the pay range for this position is between $89,300.00 and $148,700.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate's experience, education, and skill set.Bonus eligibility: ineligible.This posting is expected to remain open for at least seven days after it was posted on June 01, 2026.Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off.GE Vernova Inc. or its affiliates (collectively or individually, "GE Vernova") sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.
08/23/2026
Full time
Job Description Summary As a GE Vernova accelerator, GE Vernova Advanced Research is driving strategy and leading research & development efforts to execute on the business's mission to power the energy transition. We forge the partnerships and invent the technologies required to electrify and decarbonize for a zero-carbon future. Representing every major scientific and engineering discipline, our researchers are collaborating with GE Vernova's businesses, the U.S. government, and more than 420 technology partners to execute on 150+ energy-focused projects. Collectively, these research programs and initiatives aim to solve near term technical challenges, deliver next generation product advances, and drive long term breakthrough innovation to enable more affordable, reliable, sustainable, and secure energy. Job Description As Control Systems Research Engineer, you will enable Energy Transition through the development of models, advanced controls, optimization, estimation, and detection technologies. You will contribute to R&D programs aligned with GE Vernova's energy businesses such as Renewable Energy, Gas Power, Grid Solutions, and Power Conversion as well as with U.S. Government Agencies such as the Department of Energy and the Department of Defense. The technologies developed under these programs will be matured and transitioned into GE business products where they drive breakthrough impact for our company, our customers, and our society. As a Control Systems Research Engineer, you will: Work independently as well as in diverse teams to develop and apply advanced technology solutions to GE Vernova products and services using modeling, advanced controls, optimization, estimation, and detection technologies. Validate performance of developed solutions through simulations and application on target systems. Document technology and results through patent applications, technical reports, and publications. Stay current with advances in system technologies to seek out new ideas and applications. Work in a team environment with colleagues across GE Research, GE Vernova, and partners from industry, academia, and government agencies. Position Requirements Doctorate degree in an Engineering or related field with experience in design of advanced controls, optimization, estimation, or detection algorithms or a Master's degree in an Engineering or related field with a minimum of 3 years of experience in design of advanced controls, optimization, estimation, or detection algorithms. 3+ years of knowledge and application of advanced controls, optimization, estimation, or detection algorithms. 3+ years of proficiency in MATLAB/Simulink and C, C++, or Python. 3+ years of demonstrated experience (algorithm development and software implementation) in developing systems solutions for complex physical systems (e.g., mechanical systems, chemical plants, power systems, transportation, aviation systems). Desired Qualifications Active/current U.S. security clearance Experience with machine learning and artificial intelligence techniques and the application of those in a controls context Experience with real-time implementation of system solutions in a variety of hardware platforms Experience developing controls solutions for industrial applications Experience with machine learning and artificial intelligence techniques and the application of those in a controls context Experience developing cyber-security algorithms Exposure to industrial control hardware programming and industrial control software development Proficiency in source code management through git/github Experience with application for government funding and project proposal writing Experience with program management, which includes setting up schedules, milestones, technical reviews, tracking funding, etc. Demonstrated ability to take an innovative idea from a concept to a product Eligibility Requirements Legal authorization to work in the U.S. is required. We will not sponsor individuals at the Masters level for employment visas, now or in the future, for this job opening. Must be willing to work out of an office located in Niskayuna, NY. Ability to maintain a U.S. security clearance, prerequisite for clearance is U.S. citizenship. GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation. GE Vernova is an Equal Opportunity Employer . Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law. GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable). Relocation Assistance Provided: Yes For candidates applying to a U.S. based position, the pay range for this position is between $89,300.00 and $148,700.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate's experience, education, and skill set.Bonus eligibility: ineligible.This posting is expected to remain open for at least seven days after it was posted on June 01, 2026.Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off.GE Vernova Inc. or its affiliates (collectively or individually, "GE Vernova") sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.
Distinguished AI Engineer (Remote Eligible) At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. Capital One is open to hiring a Remote Employee for this opportunity. In this role, you will: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One. The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. Basic Qualifications: Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies At least 8 years of experience programming with Python, Go, Scala, or Java Preferred Qualifications: 8 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Remote (Regardless of Location): $244,700 - $279,200 for Distinguished AI Engineer Cambridge, MA: $269,100 - $307,200 for Distinguished AI Engineer McLean, VA: $269,100 - $307,200 for Distinguished AI Engineer New York, NY: $293,600 - $335,100 for Distinguished AI Engineer San Francisco, CA: $293,600 - $335,100 for Distinguished AI Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
08/23/2026
Full time
Distinguished AI Engineer (Remote Eligible) At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. Capital One is open to hiring a Remote Employee for this opportunity. In this role, you will: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One. The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. Basic Qualifications: Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies At least 8 years of experience programming with Python, Go, Scala, or Java Preferred Qualifications: 8 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Remote (Regardless of Location): $244,700 - $279,200 for Distinguished AI Engineer Cambridge, MA: $269,100 - $307,200 for Distinguished AI Engineer McLean, VA: $269,100 - $307,200 for Distinguished AI Engineer New York, NY: $293,600 - $335,100 for Distinguished AI Engineer San Francisco, CA: $293,600 - $335,100 for Distinguished AI Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Senior Lead AI Engineer (GenAI Platform Services)Overview:At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.Team Description:The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. In this role, you will: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. Basic Qualifications: Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies At least 6 years of experience programming with Python, Go, Scala, or JavaPreferred Qualifications: 7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peersCapital One will consider sponsoring a new qualified applicant for employment authorization for this position.The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.Cambridge, MA: $229,900 - $262,400 for Sr. Lead AI Engineer McLean, VA: $229,900 - $262,400 for Sr. Lead AI Engineer New York, NY: $250,800 - $286,200 for Sr. Lead AI Engineer San Jose, CA: $250,800 - $286,200 for Sr. Lead AI Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.For technical support or questions about Capital One's recruiting process, please send an email to One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
08/23/2026
Senior Lead AI Engineer (GenAI Platform Services)Overview:At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.Team Description:The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. In this role, you will: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. Basic Qualifications: Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies At least 6 years of experience programming with Python, Go, Scala, or JavaPreferred Qualifications: 7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peersCapital One will consider sponsoring a new qualified applicant for employment authorization for this position.The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.Cambridge, MA: $229,900 - $262,400 for Sr. Lead AI Engineer McLean, VA: $229,900 - $262,400 for Sr. Lead AI Engineer New York, NY: $250,800 - $286,200 for Sr. Lead AI Engineer San Jose, CA: $250,800 - $286,200 for Sr. Lead AI Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.For technical support or questions about Capital One's recruiting process, please send an email to One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Distinguished AI Engineer (Remote Eligible) At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. Capital One is open to hiring a Remote Employee for this opportunity. In this role, you will: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One. The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. Basic Qualifications: Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies At least 8 years of experience programming with Python, Go, Scala, or Java Preferred Qualifications: 8 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Remote (Regardless of Location): $244,700 - $279,200 for Distinguished AI Engineer Cambridge, MA: $269,100 - $307,200 for Distinguished AI Engineer McLean, VA: $269,100 - $307,200 for Distinguished AI Engineer New York, NY: $293,600 - $335,100 for Distinguished AI Engineer San Francisco, CA: $293,600 - $335,100 for Distinguished AI Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
08/23/2026
Full time
Distinguished AI Engineer (Remote Eligible) At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. Capital One is open to hiring a Remote Employee for this opportunity. In this role, you will: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One. The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. Basic Qualifications: Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies At least 8 years of experience programming with Python, Go, Scala, or Java Preferred Qualifications: 8 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Remote (Regardless of Location): $244,700 - $279,200 for Distinguished AI Engineer Cambridge, MA: $269,100 - $307,200 for Distinguished AI Engineer McLean, VA: $269,100 - $307,200 for Distinguished AI Engineer New York, NY: $293,600 - $335,100 for Distinguished AI Engineer San Francisco, CA: $293,600 - $335,100 for Distinguished AI Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) At least 4 years of experience programming with Python, Scala, or Java At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field 3+ years of experience building production-ready data pipelines that feed ML models 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 2+ years of experience developing performant, resilient, and maintainable code 2+ years of experience with data gathering and preparation for ML models 2+ years of people leader experience 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Plano, TX: $179,400 - $204,700 for Lead Machine Learning Engineer McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
08/23/2026
Full time
Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) At least 4 years of experience programming with Python, Scala, or Java At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field 3+ years of experience building production-ready data pipelines that feed ML models 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 2+ years of experience developing performant, resilient, and maintainable code 2+ years of experience with data gathering and preparation for ML models 2+ years of people leader experience 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Plano, TX: $179,400 - $204,700 for Lead Machine Learning Engineer McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) At least 4 years of experience programming with Python, Scala, or Java At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field 3+ years of experience building production-ready data pipelines that feed ML models 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 2+ years of experience developing performant, resilient, and maintainable code 2+ years of experience with data gathering and preparation for ML models 2+ years of people leader experience 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
08/23/2026
Full time
Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) At least 4 years of experience programming with Python, Scala, or Java At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field 3+ years of experience building production-ready data pipelines that feed ML models 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 2+ years of experience developing performant, resilient, and maintainable code 2+ years of experience with data gathering and preparation for ML models 2+ years of people leader experience 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Job Description Description You'll help shape the user experience for next-generation AI-powered business software. Working closely with Product, Engineering, and other UX professionals, you'll design intuitive and scalable experiences that help customers accomplish complex tasks with ease. Responsibilities Create user-centered experiences from concept through launch. Design user flows, wireframes, prototypes, and high-fidelity interfaces. Define interaction patterns and information architecture for complex workflows. Collaborate with Product Managers to translate business requirements into impactful user experiences. Partner closely with Engineering to ensure successful implementation of designs. Contribute to and evolve design systems and UX standards. Participate in research synthesis, usability testing, and design validation activities. Design experiences that leverage AI, automation, and emerging technologies. Present and defend design decisions to stakeholders and leadership. Skills Ux research, figma, information architecture, visual design, AI/GenAI Product Experience, motion design, animation experience, conversational AI, chatbot design, AI-native, design system, accessibility Top Skills Details Ux research,figma,information architecture,visual design,AI/GenAI Product Experience Additional Skills & Qualifications Bachelor's degree or equivalent experience. 5+ years of experience designing software products. Strong portfolio demonstrating UX, interaction design, and information architecture expertise. Experience working in Agile product development environments. Proficiency with Figma and modern UX design tools. Comfortable balancing user needs, business goals, and technical constraints. Strong communication and collaboration skills. Highly Preferred Experience working on products that leverage: Generative AI Conversational AI Machine Learning Intelligent Automation Motion design or animation experience. Strong visual design capabilities, including typography, layout, and modern UI patterns. Demonstrated personal or professional interest in AI technology. Experience Level Expert Level Job Type & Location This is a Contract position based out of Roseland, NJ. Pay and Benefits The pay range for this position is $75.00 - $79.00/hr. Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors. Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to specific elections, plan, or program terms. If eligible, the benefits available for this temporary role may include the following: • Medical, dental & vision • Critical Illness, Accident, and Hospital • 401(k) Retirement Plan - Pre-tax and Roth post-tax contributions available • Life Insurance (Voluntary Life & AD&D for the employee and dependents) • Short and long-term disability • Health Spending Account (HSA) • Transportation benefits • Employee Assistance Program • Time Off/Leave (PTO, Vacation or Sick Leave) Workplace Type This is a fully onsite position in Roseland,NJ. Application Deadline This position is anticipated to close on Aug 21, 2026. About TEKsystems We're partners in transformation. We help clients activate ideas and solutions to take advantage of a new world of opportunity. We are a team of 80,000 strong, working with over 6,000 clients, including 80% of the Fortune 500, across North America, Europe and Asia. As an industry leader in Full-Stack Technology Services, Talent Services, and real-world application, we work with progressive leaders to drive change. That's the power of true partnership. TEKsystems is an Allegis Group company. The company is an equal opportunity employer and will consider all applications without regards to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law. About TEKsystems and TEKsystems Global Services We're a leading provider of business and technology services. We accelerate business transformation for our customers. Our expertise in strategy, design, execution and operations unlocks business value through a range of solutions. We're a team of 80,000 strong, working with over 6,000 customers, including 80% of the Fortune 500 across North America, Europe and Asia, who partner with us for our scale, full-stack capabilities and speed. We're strategic thinkers, hands-on collaborators, helping customers capitalize on change and master the momentum of technology. We're building tomorrow by delivering business outcomes and making positive impacts in our global communities. TEKsystems and TEKsystems Global Services are Allegis Group companies. Learn more at The company is an equal opportunity employer and will consider all applications without regard to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law. San Francisco Fair Chance Ordinance: Pursuant to the San Francisco Fair Chance Ordinance, for all positions located in the city and county of San Francisco, we will consider for employment qualified applicants with arrest and conviction records. Massachusetts Lie Detector: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Use of Artificial Intelligence (AI): We may use Artificial Intelligence (AI) to support parts of our hiring process, including sourcing, screening, and evaluating candidates. AI helps assess applications and qualifications, but final decisions are made by our hiring team. By applying, you acknowledge and agree that your application may be reviewed using AI tools.
08/22/2026
Full time
Job Description Description You'll help shape the user experience for next-generation AI-powered business software. Working closely with Product, Engineering, and other UX professionals, you'll design intuitive and scalable experiences that help customers accomplish complex tasks with ease. Responsibilities Create user-centered experiences from concept through launch. Design user flows, wireframes, prototypes, and high-fidelity interfaces. Define interaction patterns and information architecture for complex workflows. Collaborate with Product Managers to translate business requirements into impactful user experiences. Partner closely with Engineering to ensure successful implementation of designs. Contribute to and evolve design systems and UX standards. Participate in research synthesis, usability testing, and design validation activities. Design experiences that leverage AI, automation, and emerging technologies. Present and defend design decisions to stakeholders and leadership. Skills Ux research, figma, information architecture, visual design, AI/GenAI Product Experience, motion design, animation experience, conversational AI, chatbot design, AI-native, design system, accessibility Top Skills Details Ux research,figma,information architecture,visual design,AI/GenAI Product Experience Additional Skills & Qualifications Bachelor's degree or equivalent experience. 5+ years of experience designing software products. Strong portfolio demonstrating UX, interaction design, and information architecture expertise. Experience working in Agile product development environments. Proficiency with Figma and modern UX design tools. Comfortable balancing user needs, business goals, and technical constraints. Strong communication and collaboration skills. Highly Preferred Experience working on products that leverage: Generative AI Conversational AI Machine Learning Intelligent Automation Motion design or animation experience. Strong visual design capabilities, including typography, layout, and modern UI patterns. Demonstrated personal or professional interest in AI technology. Experience Level Expert Level Job Type & Location This is a Contract position based out of Roseland, NJ. Pay and Benefits The pay range for this position is $75.00 - $79.00/hr. Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors. Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to specific elections, plan, or program terms. If eligible, the benefits available for this temporary role may include the following: • Medical, dental & vision • Critical Illness, Accident, and Hospital • 401(k) Retirement Plan - Pre-tax and Roth post-tax contributions available • Life Insurance (Voluntary Life & AD&D for the employee and dependents) • Short and long-term disability • Health Spending Account (HSA) • Transportation benefits • Employee Assistance Program • Time Off/Leave (PTO, Vacation or Sick Leave) Workplace Type This is a fully onsite position in Roseland,NJ. Application Deadline This position is anticipated to close on Aug 21, 2026. About TEKsystems We're partners in transformation. We help clients activate ideas and solutions to take advantage of a new world of opportunity. We are a team of 80,000 strong, working with over 6,000 clients, including 80% of the Fortune 500, across North America, Europe and Asia. As an industry leader in Full-Stack Technology Services, Talent Services, and real-world application, we work with progressive leaders to drive change. That's the power of true partnership. TEKsystems is an Allegis Group company. The company is an equal opportunity employer and will consider all applications without regards to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law. About TEKsystems and TEKsystems Global Services We're a leading provider of business and technology services. We accelerate business transformation for our customers. Our expertise in strategy, design, execution and operations unlocks business value through a range of solutions. We're a team of 80,000 strong, working with over 6,000 customers, including 80% of the Fortune 500 across North America, Europe and Asia, who partner with us for our scale, full-stack capabilities and speed. We're strategic thinkers, hands-on collaborators, helping customers capitalize on change and master the momentum of technology. We're building tomorrow by delivering business outcomes and making positive impacts in our global communities. TEKsystems and TEKsystems Global Services are Allegis Group companies. Learn more at The company is an equal opportunity employer and will consider all applications without regard to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law. San Francisco Fair Chance Ordinance: Pursuant to the San Francisco Fair Chance Ordinance, for all positions located in the city and county of San Francisco, we will consider for employment qualified applicants with arrest and conviction records. Massachusetts Lie Detector: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Use of Artificial Intelligence (AI): We may use Artificial Intelligence (AI) to support parts of our hiring process, including sourcing, screening, and evaluating candidates. AI helps assess applications and qualifications, but final decisions are made by our hiring team. By applying, you acknowledge and agree that your application may be reviewed using AI tools.
