Job Description About Quantiphi: Quantiphi is an award-winning, AI-First global digital engineering company that helps the world's leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies-we solve the problems that matter most to our clients. Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries. Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. . We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including: 21 Google Cloud Partner of the Year awards in the past 10 years 3 AWS AI/ML Partner of the Year awards 3 NVIDIA Partner of the Year awards 3 Snowflake Partner of the Year awards Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators. We are also proud to be certified as a Great Place to Work-reflecting our commitment to our people and our culture. For more details, visit: Website or LinkedIn Page Role: Senior Engagement Manager Employment type: Full Time Experience: 8+ years Location : Remote (USA) Description: We are looking for a strong EM lead with people management experience to lead a team of business analysts and engineers focused on building and driving solutions and insights to drive business efficiency and customer experience. The ideal candidate will have strong project management, leadership and cross-group collaboration skills. What You Will Do: As an Senior Engagement Manager, you'll lead the delivery management of multi-functional medium/large scale AWS programs, covering project planning, delivery assurance and verification, tracking and reporting, and risk/mitigation planning. Take accountability for our customers to ensure the program(s) realizes financial benefits and transformational outcomes, constantly identify and develop new business opportunities. Work with engineering leads in strategizing and executing multi-year roadmap on portfolio of products, applications, and services. Ensure quality execution and proactively identify potential customer issues and resolve them with speed and efficiency, to drive satisfaction, outcomes, and deliver positive customer partner experience. Coordinate, measure, monitor and communicate on program(s) health. Actively contribute to org-wide strategies, in defining and implementing those along with contributing to team's roadmap and portfolio of services in alignment to the wider organization. Maintain a relentless focus across the team on continuous improvement, planning and implementing changes to process, service and technology to transform how the team operates. Build a collaborative environment - coach/mentor team members. Recruit, motivate and develop highly skilled staffs that focuses on service delivery of key performance metrics as per the client contract. Basic Qualifications: Minimum 8 yrs of experience in delivering digital transformation projects to global clients. Experience in program management, client service, and/or management consulting. Knowledge of leading teams on projects using agile methodology and frameworks like scrum, kanban or XP, etc. Strong verbal and written communication skills with technical and business leaders at all levels within large enterprises. Aptitude in business analysis and leveraging quantitative analysis techniques for decision making. Ability to work with minimal supervision in a dynamic and time sensitive work environment. Knowledge and willingness to learn and apply emerging trends in business research, data science, artificial intelligence, machine learning, and cloud computing - AWS exposure is a value addition. Team management experience is a must. What is in it for you: Join one of the world's fastest-growing AI-first digital engineering companies and make a real impact at scale. Lead and collaborate with a high-energy team of talented, driven individuals solving complex, meaningful challenges. Work with Fortune 500 companies and disruptive innovators in a research-driven environment with 60+ patents. Stay ahead of the curve by gaining hands-on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling.
10/25/2026
Full time
Job Description About Quantiphi: Quantiphi is an award-winning, AI-First global digital engineering company that helps the world's leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies-we solve the problems that matter most to our clients. Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries. Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. . We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including: 21 Google Cloud Partner of the Year awards in the past 10 years 3 AWS AI/ML Partner of the Year awards 3 NVIDIA Partner of the Year awards 3 Snowflake Partner of the Year awards Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators. We are also proud to be certified as a Great Place to Work-reflecting our commitment to our people and our culture. For more details, visit: Website or LinkedIn Page Role: Senior Engagement Manager Employment type: Full Time Experience: 8+ years Location : Remote (USA) Description: We are looking for a strong EM lead with people management experience to lead a team of business analysts and engineers focused on building and driving solutions and insights to drive business efficiency and customer experience. The ideal candidate will have strong project management, leadership and cross-group collaboration skills. What You Will Do: As an Senior Engagement Manager, you'll lead the delivery management of multi-functional medium/large scale AWS programs, covering project planning, delivery assurance and verification, tracking and reporting, and risk/mitigation planning. Take accountability for our customers to ensure the program(s) realizes financial benefits and transformational outcomes, constantly identify and develop new business opportunities. Work with engineering leads in strategizing and executing multi-year roadmap on portfolio of products, applications, and services. Ensure quality execution and proactively identify potential customer issues and resolve them with speed and efficiency, to drive satisfaction, outcomes, and deliver positive customer partner experience. Coordinate, measure, monitor and communicate on program(s) health. Actively contribute to org-wide strategies, in defining and implementing those along with contributing to team's roadmap and portfolio of services in alignment to the wider organization. Maintain a relentless focus across the team on continuous improvement, planning and implementing changes to process, service and technology to transform how the team operates. Build a collaborative environment - coach/mentor team members. Recruit, motivate and develop highly skilled staffs that focuses on service delivery of key performance metrics as per the client contract. Basic Qualifications: Minimum 8 yrs of experience in delivering digital transformation projects to global clients. Experience in program management, client service, and/or management consulting. Knowledge of leading teams on projects using agile methodology and frameworks like scrum, kanban or XP, etc. Strong verbal and written communication skills with technical and business leaders at all levels within large enterprises. Aptitude in business analysis and leveraging quantitative analysis techniques for decision making. Ability to work with minimal supervision in a dynamic and time sensitive work environment. Knowledge and willingness to learn and apply emerging trends in business research, data science, artificial intelligence, machine learning, and cloud computing - AWS exposure is a value addition. Team management experience is a must. What is in it for you: Join one of the world's fastest-growing AI-first digital engineering companies and make a real impact at scale. Lead and collaborate with a high-energy team of talented, driven individuals solving complex, meaningful challenges. Work with Fortune 500 companies and disruptive innovators in a research-driven environment with 60+ patents. Stay ahead of the curve by gaining hands-on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling.
Job Description At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world's most innovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities. Where you can make a difference. Where no two days are the same. Role: Sr AI Developer - Telecom Transformation Location : This is 100% Onsite role in Denver CO About the role you're considering We are looking for a customer-facing Lead Architect to drive Agentic AI-driven telecom transformation programs (large scale NOC / SOC / Operations, etc.), leading customer workshops, shaping solution roadmaps, defining AWS-based architectures and advising business, technology and executive stakeholders. Your Role Serve as the primary onsite Lead Architect for Agentic AI-driven telecom transformation across NOC, SOC, network operations, service assurance and incident management domains. Lead customer workshops and discovery sessions to assess current-state NOC/SOC operating models, alarm flows, ticket lifecycles, triage processes, escalation paths and operational pain points. Define Agentic AI-driven solution architectures for alarm correlation, event deduplication, anomaly detection, root-cause analysis, incident enrichment, ticket automation and closed-loop remediation. Design integration patterns for alarms, events, incidents, topology, inventory, logs, metrics, traces and operational knowledge across telecom NOC/SOC ecosystems. Partner with AWS, customer network teams, operations teams and engineering stakeholders to design secure, scalable and cloud-native Agentic AI telecom platforms. Lead solutioning, PoCs, end-to-end technical delivery and executive presentations focused on operational efficiency, service reliability, network resilience and measurable business outcomes. Collaborate closely with Offshore teams, business, network, security, operations, product and engineering stakeholders throughout the transformation lifecycle. Your skills and experience 10+ years of experience in software development, solution delivery, or technology consulting. Practical experience defining AI, GenAI, or Agentic AI use cases for enterprise operations. Ability to translate operational challenges into agent features, workflows, user stories, and acceptance criteria. Understanding of AI agents, LLMs, RAG, prompt engineering, APIs, integrations, and human-in-the-loop controls. Strong understanding of telecom operations, particularly NOC OR (OSS, incident, ticket, and service-assurance processes). Sound knowledge of software architecture, SDLC, Agile delivery, testing, deployment, and production support. Proven experience conducting client workshops, requirement-discovery sessions, and solution demonstrations. The base compensation range for this role in the posted location is: $82,082-$193,440 Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law. The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction. These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity. It is not typical for candidates to be hired at or near the top of the posted compensation range. In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws. Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include: Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave Medical, dental, and vision coverage (or provincial healthcare coordination in Canada) Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada) Life and disability insurance Employee assistance programs Other benefits as provided by local policy and eligibility Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini's discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation. Disclaimers Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law. This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodation does not pose an undue hardship. Capgemini is committed to providing reasonable accommodation during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact. Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process. Click the following link for more information on your rights as an Applicant in the United States. -employment-opportunity-is-the-law Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.
10/25/2026
Full time
Job Description At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world's most innovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities. Where you can make a difference. Where no two days are the same. Role: Sr AI Developer - Telecom Transformation Location : This is 100% Onsite role in Denver CO About the role you're considering We are looking for a customer-facing Lead Architect to drive Agentic AI-driven telecom transformation programs (large scale NOC / SOC / Operations, etc.), leading customer workshops, shaping solution roadmaps, defining AWS-based architectures and advising business, technology and executive stakeholders. Your Role Serve as the primary onsite Lead Architect for Agentic AI-driven telecom transformation across NOC, SOC, network operations, service assurance and incident management domains. Lead customer workshops and discovery sessions to assess current-state NOC/SOC operating models, alarm flows, ticket lifecycles, triage processes, escalation paths and operational pain points. Define Agentic AI-driven solution architectures for alarm correlation, event deduplication, anomaly detection, root-cause analysis, incident enrichment, ticket automation and closed-loop remediation. Design integration patterns for alarms, events, incidents, topology, inventory, logs, metrics, traces and operational knowledge across telecom NOC/SOC ecosystems. Partner with AWS, customer network teams, operations teams and engineering stakeholders to design secure, scalable and cloud-native Agentic AI telecom platforms. Lead solutioning, PoCs, end-to-end technical delivery and executive presentations focused on operational efficiency, service reliability, network resilience and measurable business outcomes. Collaborate closely with Offshore teams, business, network, security, operations, product and engineering stakeholders throughout the transformation lifecycle. Your skills and experience 10+ years of experience in software development, solution delivery, or technology consulting. Practical experience defining AI, GenAI, or Agentic AI use cases for enterprise operations. Ability to translate operational challenges into agent features, workflows, user stories, and acceptance criteria. Understanding of AI agents, LLMs, RAG, prompt engineering, APIs, integrations, and human-in-the-loop controls. Strong understanding of telecom operations, particularly NOC OR (OSS, incident, ticket, and service-assurance processes). Sound knowledge of software architecture, SDLC, Agile delivery, testing, deployment, and production support. Proven experience conducting client workshops, requirement-discovery sessions, and solution demonstrations. The base compensation range for this role in the posted location is: $82,082-$193,440 Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law. The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction. These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity. It is not typical for candidates to be hired at or near the top of the posted compensation range. In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws. Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include: Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave Medical, dental, and vision coverage (or provincial healthcare coordination in Canada) Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada) Life and disability insurance Employee assistance programs Other benefits as provided by local policy and eligibility Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini's discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation. Disclaimers Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law. This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodation does not pose an undue hardship. Capgemini is committed to providing reasonable accommodation during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact. Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process. Click the following link for more information on your rights as an Applicant in the United States. -employment-opportunity-is-the-law Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.
Job Description Title - Principal AI Architect - Enterprise AI Delivery Location : Newark ,NJ (Hybrid-3 days in a week) Role - Full Time with SG analytics Experience level - 12+ years Mandatory - Team management : 5 to 30+ AI engineers and data scientists Skill required - Open source AI,Open-weight model , LoRA or QLoRA, Python, NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act's risk tiers ,Databricks or Snowflake, Kubernetes ,generative and agentic production work , Why this role exists Most enterprise AI never leaves the pilot. Roughly 80% of initiatives fail to deliver their promised value, and only about one in seven organizations has scaled an agentic system to real operational use. The failure is almost never the model. It is curated pilot data that does not survive production, an integration layer underestimated by an order of magnitude, and evaluation infrastructure that was never built. We close that gap inside our clients' environments at a cost per unit of output that stays defensible as volume grows. That last part is why this is not a frontier-API orchestration job: the economics only work when someone can fine-tune and serve open-weight models where they beat a hosted call. This is the most consequential technical hire on our team. What you will own Architecture authority and final escalation. Set the reference architecture for AI systems we deploy into client environments - model selection and routing, retrieval design, tool exposure, orchestration, guardrails, observability, and evaluation. Be the last stop on hard architecture, performance, and deployment calls. Production, not proofs of concept. Own the standards that get prototypes to production: design and code reviews, automated testing, quality gates, release-readiness checks, and integration with clients' real systems of record and identity. Open-weight model engineering and unit economics. We price per unit of output, which makes cost per inference a margin decision. Own what to fine-tune, on what base, with what data, and how to serve it - and the discipline of proving the tuned model beats the frontier baseline it replaces. Then routing, caching, quantization, batching, and distillation on top. Team capability and reusable IP. Lead 30+ engineers and data scientists across concurrent client implementations, and build the reference architectures and accelerators that make the second implementation cost materially less than the first. Success is a team that solves hard problems without you. Commercial partnership. Work with sales and consulting on client workshops, proposals, effort and cost estimation, and technical due diligence - assessing feasibility honestly, including when the answer is no. What we are screening for Evidence of production, at scale. You have designed and shipped AI systems that ran in production against real enterprise data and real users, and can walk through the trade-offs that kept them working. Pilots and demos do not count. Fine-tuning open-weight models for production. You have taken an open-weight model, curated the training data, chosen a full fine-tune against LoRA or QLoRA deliberately, evaluated it against the frontier baseline, and served it under real load. Candidates whose entire experience is calling hosted frontier APIs are not a fit for this role. Cost and performance engineering. You have measurably reduced the running cost or latency of an AI system and can describe how, with numbers - quantization, distillation, routing, caching, or serving changes. Evaluation as infrastructure. You treat evals as a first-class engineering concern - offline suites, regression gates in CI, online monitoring, and human review. If observability is where you stop, this role is not a fit. Data readiness and governance. You can look at an enterprise's data estate and say whether it supports the proposed system before the build starts. You have working command of the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act's risk tiers - and, for financial services, model risk management practice as it now stretches to generative and agentic systems. Hands-on depth and team leadership. You still write and review code. Python assumes, plus distributed systems, APIs, containers and Kubernetes,CI/CD, GPU serving, one major cloud, and platforms such as Databricks or Snowflake. You have made senior engineers better and can point to people who outgrew needing you. Baseline : 12+ years across software engineering, architecture, data platforms, and AI/ML, including time in a startup or product company - we need engineering velocity, not only enterprise processes. Degree in Computer Science, Engineering, or a related technical field. No certifications required preferred - we are interested in what you have shipped. What this role is NOT We have received strong candidates for adjacent roles who are not right for this one. To be explicit: Not this role Why Frontier-API integrator - If every system you built was a prompt and a hosted API call, you have not done the engineering this role requires. Data architect - Warehouse, lakehouse, and pipeline design is a prerequisite here, not the job Cloud / platform architect - Cloud-native infrastructure depth without model-level engineering is not a fit ML engineer, classical only- Predictive-modeling depth without generative and agentic production work is insufficient Delivery or engineering manager - If you have stopped writing and reviewing code, this is the wrong role regardless of seniority
10/25/2026
Full time
Job Description Title - Principal AI Architect - Enterprise AI Delivery Location : Newark ,NJ (Hybrid-3 days in a week) Role - Full Time with SG analytics Experience level - 12+ years Mandatory - Team management : 5 to 30+ AI engineers and data scientists Skill required - Open source AI,Open-weight model , LoRA or QLoRA, Python, NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act's risk tiers ,Databricks or Snowflake, Kubernetes ,generative and agentic production work , Why this role exists Most enterprise AI never leaves the pilot. Roughly 80% of initiatives fail to deliver their promised value, and only about one in seven organizations has scaled an agentic system to real operational use. The failure is almost never the model. It is curated pilot data that does not survive production, an integration layer underestimated by an order of magnitude, and evaluation infrastructure that was never built. We close that gap inside our clients' environments at a cost per unit of output that stays defensible as volume grows. That last part is why this is not a frontier-API orchestration job: the economics only work when someone can fine-tune and serve open-weight models where they beat a hosted call. This is the most consequential technical hire on our team. What you will own Architecture authority and final escalation. Set the reference architecture for AI systems we deploy into client environments - model selection and routing, retrieval design, tool exposure, orchestration, guardrails, observability, and evaluation. Be the last stop on hard architecture, performance, and deployment calls. Production, not proofs of concept. Own the standards that get prototypes to production: design and code reviews, automated testing, quality gates, release-readiness checks, and integration with clients' real systems of record and identity. Open-weight model engineering and unit economics. We price per unit of output, which makes cost per inference a margin decision. Own what to fine-tune, on what base, with what data, and how to serve it - and the discipline of proving the tuned model beats the frontier baseline it replaces. Then routing, caching, quantization, batching, and distillation on top. Team capability and reusable IP. Lead 30+ engineers and data scientists across concurrent client implementations, and build the reference architectures and accelerators that make the second implementation cost materially less than the first. Success is a team that solves hard problems without you. Commercial partnership. Work with sales and consulting on client workshops, proposals, effort and cost estimation, and technical due diligence - assessing feasibility honestly, including when the answer is no. What we are screening for Evidence of production, at scale. You have designed and shipped AI systems that ran in production against real enterprise data and real users, and can walk through the trade-offs that kept them working. Pilots and demos do not count. Fine-tuning open-weight models for production. You have taken an open-weight model, curated the training data, chosen a full fine-tune against LoRA or QLoRA deliberately, evaluated it against the frontier baseline, and served it under real load. Candidates whose entire experience is calling hosted frontier APIs are not a fit for this role. Cost and performance engineering. You have measurably reduced the running cost or latency of an AI system and can describe how, with numbers - quantization, distillation, routing, caching, or serving changes. Evaluation as infrastructure. You treat evals as a first-class engineering concern - offline suites, regression gates in CI, online monitoring, and human review. If observability is where you stop, this role is not a fit. Data readiness and governance. You can look at an enterprise's data estate and say whether it supports the proposed system before the build starts. You have working command of the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act's risk tiers - and, for financial services, model risk management practice as it now stretches to generative and agentic systems. Hands-on depth and team leadership. You still write and review code. Python assumes, plus distributed systems, APIs, containers and Kubernetes,CI/CD, GPU serving, one major cloud, and platforms such as Databricks or Snowflake. You have made senior engineers better and can point to people who outgrew needing you. Baseline : 12+ years across software engineering, architecture, data platforms, and AI/ML, including time in a startup or product company - we need engineering velocity, not only enterprise processes. Degree in Computer Science, Engineering, or a related technical field. No certifications required preferred - we are interested in what you have shipped. What this role is NOT We have received strong candidates for adjacent roles who are not right for this one. To be explicit: Not this role Why Frontier-API integrator - If every system you built was a prompt and a hosted API call, you have not done the engineering this role requires. Data architect - Warehouse, lakehouse, and pipeline design is a prerequisite here, not the job Cloud / platform architect - Cloud-native infrastructure depth without model-level engineering is not a fit ML engineer, classical only- Predictive-modeling depth without generative and agentic production work is insufficient Delivery or engineering manager - If you have stopped writing and reviewing code, this is the wrong role regardless of seniority
Job Description Senior / Staff Member of Technical Staff Employment Type: Full-time Experience: 4+ years Focus: AI Product Engineering, Full-Stack Development, Distributed Systems, APIs, Customer Research Platforms About Our Client Our client is building an AI-native customer research platform that transforms how companies understand their customers and make business decisions. Their AI agents automate the entire research process, from designing studies and recruiting participants to conducting interviews and synthesizing insights. The platform connects companies with a network of more than 50 million research participants, supports interviews in over 120 languages, and helps organizations gather insights at unprecedented speed and scale. The company is also developing AI-powered digital twins that simulate real customer behavior, enabling businesses to test products, messaging, and strategic decisions before bringing them to market. Trusted by major technology companies, including Microsoft and Anthropic, the platform is designed to fundamentally change how customer research is conducted. About the Role Our client is hiring a Senior / Staff Member of Technical Staff to build and scale its AI-powered customer research platform. This is a high-ownership engineering role that combines full-stack product development, AI-powered features, platform engineering, and user experience. You'll work across the product, from building conversational AI experiences and research insights to developing participant recruitment systems, improving platform design, and scaling backend infrastructure. The ideal candidate is a product-minded software engineer who enjoys building customer-facing applications, solving complex technical problems, and owning features from concept through production. What You'll Do Build and ship AI-native product features across the customer research platform. Develop conversational AI capabilities that allow customers to interact with research findings and insights. Build analysis and insights tools that help businesses understand customer feedback and make decisions. Develop participant recruitment, incentive management, and self-service research workflows. Build systems for managing research panels, participant lists, and recruitment at scale. Improve the overall product experience, including frontend architecture, UI components, and design systems. Partner closely with product and design teams to develop intuitive, customer-facing experiences. Design and build scalable APIs, backend services, and distributed systems. Own features end-to-end, from technical design and implementation through deployment and iteration. Help expand the product roadmap and improve the platform as customer adoption grows. What We're Looking For 4+ years of professional software engineering experience, with demonstrated senior-level technical ability. Strong full-stack software engineering fundamentals. Experience building and shipping production software used by real customers. Strong product instincts and ability to translate ambiguous requirements into intuitive user experiences. Experience developing backend APIs, services, and scalable application architecture. Comfortable collaborating with product managers, designers, and engineers. Ability to take ownership of complex technical problems from design through deployment. Experience working with modern frontend frameworks and backend technologies. Strong understanding of system design, software architecture, and engineering best practices. Ability to move quickly, solve problems independently, and thrive in a high-performance engineering environment. Bonus Experience Experience building AI-native products or LLM-powered applications. Experience with conversational AI, agentic workflows, or AI-driven analytics. Experience building data analysis, insights, or business intelligence products. Experience developing self-service platforms, marketplaces, or participant recruitment systems. Experience with distributed systems and high-scale backend infrastructure. Experience building frontend design systems or complex interactive interfaces. Previous experience at an early-stage or high-growth startup. Why This Opportunity Build AI agents that automate customer research from start to finish. Work on products used by some of the world's most recognized technology companies. Join a small, highly technical team building an AI-native platform. Own meaningful product features across frontend, backend, and AI systems. Help develop new ways for companies to understand customers through AI-powered research. Experience startup-style engineering with the potential reach and resources of a global enterprise software company. Help shape a platform positioned to serve a significantly larger enterprise customer base.
10/25/2026
Full time
Job Description Senior / Staff Member of Technical Staff Employment Type: Full-time Experience: 4+ years Focus: AI Product Engineering, Full-Stack Development, Distributed Systems, APIs, Customer Research Platforms About Our Client Our client is building an AI-native customer research platform that transforms how companies understand their customers and make business decisions. Their AI agents automate the entire research process, from designing studies and recruiting participants to conducting interviews and synthesizing insights. The platform connects companies with a network of more than 50 million research participants, supports interviews in over 120 languages, and helps organizations gather insights at unprecedented speed and scale. The company is also developing AI-powered digital twins that simulate real customer behavior, enabling businesses to test products, messaging, and strategic decisions before bringing them to market. Trusted by major technology companies, including Microsoft and Anthropic, the platform is designed to fundamentally change how customer research is conducted. About the Role Our client is hiring a Senior / Staff Member of Technical Staff to build and scale its AI-powered customer research platform. This is a high-ownership engineering role that combines full-stack product development, AI-powered features, platform engineering, and user experience. You'll work across the product, from building conversational AI experiences and research insights to developing participant recruitment systems, improving platform design, and scaling backend infrastructure. The ideal candidate is a product-minded software engineer who enjoys building customer-facing applications, solving complex technical problems, and owning features from concept through production. What You'll Do Build and ship AI-native product features across the customer research platform. Develop conversational AI capabilities that allow customers to interact with research findings and insights. Build analysis and insights tools that help businesses understand customer feedback and make decisions. Develop participant recruitment, incentive management, and self-service research workflows. Build systems for managing research panels, participant lists, and recruitment at scale. Improve the overall product experience, including frontend architecture, UI components, and design systems. Partner closely with product and design teams to develop intuitive, customer-facing experiences. Design and build scalable APIs, backend services, and distributed systems. Own features end-to-end, from technical design and implementation through deployment and iteration. Help expand the product roadmap and improve the platform as customer adoption grows. What We're Looking For 4+ years of professional software engineering experience, with demonstrated senior-level technical ability. Strong full-stack software engineering fundamentals. Experience building and shipping production software used by real customers. Strong product instincts and ability to translate ambiguous requirements into intuitive user experiences. Experience developing backend APIs, services, and scalable application architecture. Comfortable collaborating with product managers, designers, and engineers. Ability to take ownership of complex technical problems from design through deployment. Experience working with modern frontend frameworks and backend technologies. Strong understanding of system design, software architecture, and engineering best practices. Ability to move quickly, solve problems independently, and thrive in a high-performance engineering environment. Bonus Experience Experience building AI-native products or LLM-powered applications. Experience with conversational AI, agentic workflows, or AI-driven analytics. Experience building data analysis, insights, or business intelligence products. Experience developing self-service platforms, marketplaces, or participant recruitment systems. Experience with distributed systems and high-scale backend infrastructure. Experience building frontend design systems or complex interactive interfaces. Previous experience at an early-stage or high-growth startup. Why This Opportunity Build AI agents that automate customer research from start to finish. Work on products used by some of the world's most recognized technology companies. Join a small, highly technical team building an AI-native platform. Own meaningful product features across frontend, backend, and AI systems. Help develop new ways for companies to understand customers through AI-powered research. Experience startup-style engineering with the potential reach and resources of a global enterprise software company. Help shape a platform positioned to serve a significantly larger enterprise customer base.
Job Description Title : AI Architect Location : Nutley, NJ or NY Duration : 12+ Months Rate : OPEN Visa Status : Any In-Person Interview : No Relevant Experience (in Yrs.) : 5+ Detailed Job Description: We are seeking an experienced AI Architect to design, architect, and lead the implementation of enterprise-scale Artificial Intelligence (AI), Machine Learning (ML), Generative AI, and Agentic AI solutions. The ideal candidate will have strong expertise in AI architecture, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), multi-agent systems, cloud AI platforms, and enterprise application integration. The AI Architect will collaborate with business stakeholders, data scientists, ML engineers, software developers, security teams, and enterprise architects to translate business requirements into scalable, secure, reliable, and cost-effective AI solutions. Key Responsibilities Define enterprise AI architecture, reference architectures, technical standards, and implementation roadmaps. Design and implement Generative AI solutions using LLMs, prompt engineering, RAG, embeddings, vector databases, and semantic search. Architect AI agents and multi-agent workflows using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent technologies. Evaluate and integrate foundation models from OpenAI, Azure OpenAI, AWS Bedrock, Google Vertex AI, Anthropic, and other AI platforms. Design scalable AI services and integrations using Python, REST APIs, microservices, and event-driven architectures. Build enterprise knowledge retrieval solutions using vector databases and enterprise data sources. Define MLOps and LLMOps practices covering model deployment, CI/CD, automated evaluation, monitoring, versioning, and lifecycle management. Establish AI security and governance standards, including responsible AI, data privacy, access control, model risk management, and regulatory compliance. Optimize AI solutions for performance, latency, scalability, token consumption, and cloud operating costs. Collaborate with business and technical teams to identify AI use cases, conduct proof of concepts, and transition successful solutions into production. Establish evaluation frameworks to measure model accuracy, hallucination rates, relevance, reliability, and business outcomes. Mentor AI/ML engineers and provide technical leadership throughout solution design, development, deployment, and operations. Thanks, and Regards Prasanna Kumar Senior Talent Acquisition Lead Ascii Group LLC Email: Phone: +1 / Ext 112
10/25/2026
Full time
Job Description Title : AI Architect Location : Nutley, NJ or NY Duration : 12+ Months Rate : OPEN Visa Status : Any In-Person Interview : No Relevant Experience (in Yrs.) : 5+ Detailed Job Description: We are seeking an experienced AI Architect to design, architect, and lead the implementation of enterprise-scale Artificial Intelligence (AI), Machine Learning (ML), Generative AI, and Agentic AI solutions. The ideal candidate will have strong expertise in AI architecture, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), multi-agent systems, cloud AI platforms, and enterprise application integration. The AI Architect will collaborate with business stakeholders, data scientists, ML engineers, software developers, security teams, and enterprise architects to translate business requirements into scalable, secure, reliable, and cost-effective AI solutions. Key Responsibilities Define enterprise AI architecture, reference architectures, technical standards, and implementation roadmaps. Design and implement Generative AI solutions using LLMs, prompt engineering, RAG, embeddings, vector databases, and semantic search. Architect AI agents and multi-agent workflows using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent technologies. Evaluate and integrate foundation models from OpenAI, Azure OpenAI, AWS Bedrock, Google Vertex AI, Anthropic, and other AI platforms. Design scalable AI services and integrations using Python, REST APIs, microservices, and event-driven architectures. Build enterprise knowledge retrieval solutions using vector databases and enterprise data sources. Define MLOps and LLMOps practices covering model deployment, CI/CD, automated evaluation, monitoring, versioning, and lifecycle management. Establish AI security and governance standards, including responsible AI, data privacy, access control, model risk management, and regulatory compliance. Optimize AI solutions for performance, latency, scalability, token consumption, and cloud operating costs. Collaborate with business and technical teams to identify AI use cases, conduct proof of concepts, and transition successful solutions into production. Establish evaluation frameworks to measure model accuracy, hallucination rates, relevance, reliability, and business outcomes. Mentor AI/ML engineers and provide technical leadership throughout solution design, development, deployment, and operations. Thanks, and Regards Prasanna Kumar Senior Talent Acquisition Lead Ascii Group LLC Email: Phone: +1 / Ext 112
Job Description We are At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron's progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 16,700, and has 57offices in 22 countries within key global markets. Our challenge We are seeking an experienced AI Architect to lead the design, development, and implementation of enterprise-scale AI and Generative AI solutions. The ideal candidate will have deep expertise in AI/ML architectures, Large Language Models (LLMs), Agentic AI frameworks, cloud platforms, and modern AI development ecosystems. The candidate will drive AI strategy, establish architectural standards, and collaborate with business and technology teams to deliver scalable, secure, and responsible AI solutions. Additional Information The base salary for this position will vary based on geography and other factors. In accordance with law, the base salary for this role if filled within Charlotte, NC is $125K - $130K/year & benefits (see below). The Role Responsibilities: Design and implement enterprise AI and Generative AI architectures. Define AI technology roadmaps, standards, and best practices. Architect LLM-based applications using Agentic AI frameworks such as LangChain and LangGraph. Lead AI solution design across machine learning, NLP, computer vision, and GenAI use cases. Establish RAG (Retrieval-Augmented Generation) architectures for enterprise knowledge solutions. Design AI platforms leveraging Azure AI Services, Azure OpenAI, AWS Bedrock, or GCP Vertex AI. Define governance, security, compliance, and responsible AI frameworks. Collaborate with business stakeholders to identify AI opportunities and translate them into technical solutions. Provide architectural guidance for model selection, prompt engineering, fine-tuning, and deployment strategies. Lead AI performance optimization, scalability, monitoring, and observability initiatives. Mentor development teams and promote AI engineering best practices. Drive enterprise-wide AI adoption and innovation programs. Requirements: 12+ years of software engineering and architecture experience. Strong expertise in Generative AI, Machine Learning, and Artificial Intelligence solutions. Hands-on experience with OpenAI, Azure OpenAI, Anthropic Claude, Gemini, or similar LLMs. Expertise in LangChain, LangGraph, Semantic Kernel, CrewAI, or Agentic AI frameworks. Experience designing RAG, Vector Database, and Knowledge Management solutions. Strong programming skills in Python. Experience with vector databases such as Pinecone, Weaviate, Chroma, or Azure AI Search. Expertise in AI model deployment, MLOps, and AI lifecycle management. Experience with Azure, AWS, or GCP cloud platforms. Strong understanding of AI governance, responsible AI, model monitoring, and security. Experience with API architecture, microservices, and enterprise integration patterns. Excellent stakeholder management, leadership, and communication skills. Preferred, but not required: Financial Services or Banking domain experience. Experience with AI agents, autonomous workflows, and multi-agent systems. Knowledge of Kubernetes, Docker, and cloud-native architectures. Experience implementing enterprise AI Centers of Excellence (CoE). AI/Cloud certifications (Azure AI Engineer, AWS ML Specialty, Google Professional ML Engineer). We offer: A highly competitive compensation and benefits package. A multinational organization with 57 offices in 22 countries and the possibility to work abroad. 10 days of paid annual leave (plus sick leave and national holidays). Maternity & paternity leave plans. A comprehensive insurance plan including medical, dental, vision, life insurance, and long-/short-term disability (plans vary by region). Retirement savings plans. A higher education certification policy. Commuter benefits (varies by region). Extensive training opportunities, focused on skills, substantive knowledge, and personal development. On-demand Udemy for Business for all Synechron employees with free access to more than 5000 curated courses. Coaching opportunities with experienced colleagues from our Financial Innovation Labs (FinLabs) and Center of Excellences (CoE) groups. Cutting edge projects at the world's leading tier-one banks, financial institutions and insurance firms. A flat and approachable organization. A truly diverse, fun-loving, and global work culture.
10/24/2026
Full time
Job Description We are At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron's progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 16,700, and has 57offices in 22 countries within key global markets. Our challenge We are seeking an experienced AI Architect to lead the design, development, and implementation of enterprise-scale AI and Generative AI solutions. The ideal candidate will have deep expertise in AI/ML architectures, Large Language Models (LLMs), Agentic AI frameworks, cloud platforms, and modern AI development ecosystems. The candidate will drive AI strategy, establish architectural standards, and collaborate with business and technology teams to deliver scalable, secure, and responsible AI solutions. Additional Information The base salary for this position will vary based on geography and other factors. In accordance with law, the base salary for this role if filled within Charlotte, NC is $125K - $130K/year & benefits (see below). The Role Responsibilities: Design and implement enterprise AI and Generative AI architectures. Define AI technology roadmaps, standards, and best practices. Architect LLM-based applications using Agentic AI frameworks such as LangChain and LangGraph. Lead AI solution design across machine learning, NLP, computer vision, and GenAI use cases. Establish RAG (Retrieval-Augmented Generation) architectures for enterprise knowledge solutions. Design AI platforms leveraging Azure AI Services, Azure OpenAI, AWS Bedrock, or GCP Vertex AI. Define governance, security, compliance, and responsible AI frameworks. Collaborate with business stakeholders to identify AI opportunities and translate them into technical solutions. Provide architectural guidance for model selection, prompt engineering, fine-tuning, and deployment strategies. Lead AI performance optimization, scalability, monitoring, and observability initiatives. Mentor development teams and promote AI engineering best practices. Drive enterprise-wide AI adoption and innovation programs. Requirements: 12+ years of software engineering and architecture experience. Strong expertise in Generative AI, Machine Learning, and Artificial Intelligence solutions. Hands-on experience with OpenAI, Azure OpenAI, Anthropic Claude, Gemini, or similar LLMs. Expertise in LangChain, LangGraph, Semantic Kernel, CrewAI, or Agentic AI frameworks. Experience designing RAG, Vector Database, and Knowledge Management solutions. Strong programming skills in Python. Experience with vector databases such as Pinecone, Weaviate, Chroma, or Azure AI Search. Expertise in AI model deployment, MLOps, and AI lifecycle management. Experience with Azure, AWS, or GCP cloud platforms. Strong understanding of AI governance, responsible AI, model monitoring, and security. Experience with API architecture, microservices, and enterprise integration patterns. Excellent stakeholder management, leadership, and communication skills. Preferred, but not required: Financial Services or Banking domain experience. Experience with AI agents, autonomous workflows, and multi-agent systems. Knowledge of Kubernetes, Docker, and cloud-native architectures. Experience implementing enterprise AI Centers of Excellence (CoE). AI/Cloud certifications (Azure AI Engineer, AWS ML Specialty, Google Professional ML Engineer). We offer: A highly competitive compensation and benefits package. A multinational organization with 57 offices in 22 countries and the possibility to work abroad. 10 days of paid annual leave (plus sick leave and national holidays). Maternity & paternity leave plans. A comprehensive insurance plan including medical, dental, vision, life insurance, and long-/short-term disability (plans vary by region). Retirement savings plans. A higher education certification policy. Commuter benefits (varies by region). Extensive training opportunities, focused on skills, substantive knowledge, and personal development. On-demand Udemy for Business for all Synechron employees with free access to more than 5000 curated courses. Coaching opportunities with experienced colleagues from our Financial Innovation Labs (FinLabs) and Center of Excellences (CoE) groups. Cutting edge projects at the world's leading tier-one banks, financial institutions and insurance firms. A flat and approachable organization. A truly diverse, fun-loving, and global work culture.
Job Description AI Engineer (Forward Deployed) SF/NYC / Travel / $200k-$400k + early stage equity My client is looking for a Forward Deployed Engineer to sit at the intersection of engineering and customer delivery, building production AI systems directly with enterprise clients. You'll spend your time in customer environments, not just shipping code from behind a product spec, turning ambiguous real-world problems into working software that customers actually use. Day to day, that means building and deploying LLM-powered and agentic systems, retrieval-augmented applications, and getting GenAI features from prototype into reliable production use. You'll own the evaluation and reliability side too, not just the initial build, so what you ship keeps working after you leave the room. Strong fit for engineers coming from client-facing, applied-AI or consulting-led backgrounds, particularly those with experience at top-tier strategy or technical consulting firms. A strong CS degree is expected. What My Client Is Looking For: 3+ years of industry experience Comfortable operating with ambiguity and directly in front of customers Strong Python and/or TypeScript skills in production environments Track record deploying data-heavy or AI-enabled applications Able to communicate clearly across technical and non-technical audiences Open to travel, roughly 20 percent Wants the pace and ownership of a startup environment Nice to Have: Prior forward-deployed or customer-facing engineering role Hands-on production experience with ML, LLMs, or agentic systems Frontend experience, React preferred
10/24/2026
Full time
Job Description AI Engineer (Forward Deployed) SF/NYC / Travel / $200k-$400k + early stage equity My client is looking for a Forward Deployed Engineer to sit at the intersection of engineering and customer delivery, building production AI systems directly with enterprise clients. You'll spend your time in customer environments, not just shipping code from behind a product spec, turning ambiguous real-world problems into working software that customers actually use. Day to day, that means building and deploying LLM-powered and agentic systems, retrieval-augmented applications, and getting GenAI features from prototype into reliable production use. You'll own the evaluation and reliability side too, not just the initial build, so what you ship keeps working after you leave the room. Strong fit for engineers coming from client-facing, applied-AI or consulting-led backgrounds, particularly those with experience at top-tier strategy or technical consulting firms. A strong CS degree is expected. What My Client Is Looking For: 3+ years of industry experience Comfortable operating with ambiguity and directly in front of customers Strong Python and/or TypeScript skills in production environments Track record deploying data-heavy or AI-enabled applications Able to communicate clearly across technical and non-technical audiences Open to travel, roughly 20 percent Wants the pace and ownership of a startup environment Nice to Have: Prior forward-deployed or customer-facing engineering role Hands-on production experience with ML, LLMs, or agentic systems Frontend experience, React preferred
Job Description Job Description Skill: Technical Architect Must Have Technical/Functional Skills: 6-7 years experience across cloud, infrastructure engineering, production operations, platform engineering, SRE, DevOps, or enterprise technology transformation. At least 1-2 years in architecture, program ownership, or technology partner roles with accountability for critical platforms, budgets, vendors, regulatory commitments, and distributed teams. Strong expertise across Microsoft Azure and AWS, including enterprise landing zones, identity, networking, compute, storage, databases, containers, security, observability, resilience, governance, and cost management. Preferred DevOps engineering background with credible hands-on experience in CI/CD, GitOps, Infrastructure as Code, automation, scripting, containers, orchestration, observability, release engineering, and production support. Proven leadership of cloud adoption, data-center modernization, hybrid-cloud transformation, migration, platform engineering, resilience, automation, technical-debt reduction, or operating-model change. Deep knowledge of network and infrastructure architecture, identity and access management, backup and recovery, capacity, performance, virtualization, middleware, databases, patching, vulnerability management, and lifecycle governance. Demonstrated command of incident, problem, change, release, availability, configuration, continuity, disaster recovery, major-incident, and service-level management. Strong understanding of DevSecOps, zero trust, cloud security controls, operational resilience, audit evidence, technology risk, compliance, and third-party governance in banking or financial services. Excellent executive communication, stakeholder influence, vendor and commercial management, negotiation, financial management, facilitation, and organizational leadership skills. Ability to work onsite as required, lead across time zones, and provide executive and technical direction during critical incidents or planned changes. Significant experience in banking, financial services, payments, lending, deposits, risk, treasury, digital banking, or another highly regulated and high-availability environment. Demonstrated delivery of enterprise AI/ML, generative AI, agentic AI, AIOps, MLOps, or AI-enabled engineering and operations initiatives. Experience integrating Azure AI Foundry/Azure OpenAI, Azure Machine Learning, Amazon Bedrock, Amazon SageMaker, or comparable enterprise AI services into secure platforms and operating workflows. Knowledge of responsible AI, model governance, data privacy, AI evaluation, retrieval-augmented generation, prompt and model lifecycle management, human oversight, and production AI monitoring. Experience applying AI to anomaly detection, predictive incident management, event correlation, root-cause analysis, capacity forecasting, operational knowledge, engineering productivity, and automated remediation. Relevant certifications such as Azure Solutions Architect Expert, Azure DevOps Engineer Expert, AWS Solutions Architect Professional, AWS DevOps Engineer Professional, Kubernetes, Terraform, ITIL, SRE, FinOps, or cloud security credentials. Experience with ServiceNow, Splunk, Dynatrace, AppDynamics, Grafana, Prometheus, Azure Monitor, Amazon CloudWatch, OpenTelemetry, or comparable observability and service-management platforms. Experience establishing internal developer platforms, cloud centers of excellence, reusable engineering patterns, service catalogs, golden paths, and communities of practice. Roles & Responsibilities: Cloud Strategy & Architecture: Define and execute the bank's Azure and AWS strategy, target architecture, landing zones, account/subscription model, guardrails, shared services, and migration roadmap. Design secure, resilient, scalable, and cost-effective hybrid and multi-cloud solutions across compute, storage, networking, databases, integration, identity, containers, and serverless services. Lead workload assessments and select appropriate rehost, replatform, refactor, retire, retain, or replace strategies. Establish reusable architecture patterns, golden paths, reference implementations, policy-as-code, and technical standards for regulated workloads. Conduct architecture reviews and ensure alignment with enterprise architecture, security, data, operational resilience, and regulatory requirements. Infrastructure, Operations & Resilience: Own the strategic direction for hybrid infrastructure, including network, compute, storage, virtualization, identity, backup, database platforms, middleware, end-user dependencies, and data-center integrations. Improve operational maturity across incident, problem, change, release, capacity, availability, configuration, patch, vulnerability, backup, and service-continuity management. Define service-level objectives, reliability indicators, error budgets, recovery objectives, operational readiness criteria, escalation paths, and executive service reporting. Lead major incident response, root-cause analysis, corrective actions, resilience testing, disaster-recovery exercises, and systemic risk reduction. Implement end-to-end observability, event correlation, actionable alerting, service mapping, capacity forecasting, and automated remediation. Drive simplification, standardization, technical-debt reduction, lifecycle management, platform currency, and retirement of unsupported technologies. DevOps, Platform Engineering & Automation Champion a DevOps engineering culture based on shared ownership, secure automation, continuous improvement, rapid feedback, and frequent low-risk releases. Design and govern CI/CD and GitOps practices using Azure DevOps, GitHub Actions, GitLab CI, Jenkins, or equivalent enterprise tooling. Drive Infrastructure as Code and configuration automation using Terraform, Bicep/ARM, CloudFormation, Ansible, PowerShell, Python, or Bash. Lead container and orchestration adoption using Docker, Kubernetes, AKS, EKS, and related platform services. Embed automated testing, code quality, secrets management, vulnerability scanning, artifact controls, policy checks, segregation of duties, and deployment approvals into pipelines. Track engineering outcomes through deployment frequency, lead time for change, change failure rate, recovery time, availability, automation coverage, platform adoption, and technical-debt reduction. AI Integration & Intelligent Operations - Preferred: Identify and prioritize responsible AI, generative AI, and machine-learning opportunities across IT operations, engineering productivity, service management, knowledge management, fraud/risk support, and customer or employee experiences. Integrate AI capabilities using services such as Azure AI Foundry/Azure OpenAI, Azure Machine Learning, Amazon Bedrock, Amazon SageMaker, or comparable enterprise platforms. Support MLOps and GenAIOps practices, including model and prompt versioning, automated pipe lines, evaluation, deployment, observability, drift detection, grounding, rollback, and lifecycle controls. Apply secure and responsible AI principles covering privacy, data lineage, access control, human oversight, output validation, explainability, model risk, prompt-injection defense, and confidential-data protection. Evaluate AIOps use cases for anomaly detection, predictive incident management, alert reduction, root-cause analysis, capacity forecasting, and self-healing operations. Partner with data, cybersecurity, legal, compliance, model-risk, and business teams to move AI proofs of value into governed, production-ready services. Security, Risk, Compliance & FinOps: Partner with cybersecurity, enterprise architecture, operational risk, audit, compliance, data governance, and model-risk teams to implement controls aligned with bank policy and applicable regulatory expectations. Apply zero-trust, least-privilege, segregation-of-duties, encryption, key management, privileged-access, secure configuration, immutable logging, and continuous-monitoring principles. Maintain evidence-ready controls and support risk assessments, technology control testing, audits, remediation plans, regulatory examinations, and third-party reviews. Implement FinOps practices for budgeting, tagging, showback/chargeback, commitment management, rightsizing, waste reduction, licensing optimization, and unit-cost transparency. Manage vendor performance, statements of work, service levels, architectural compliance, delivery risks, commercial outcomes, and knowledge transfer.
10/24/2026
Full time
Job Description Job Description Skill: Technical Architect Must Have Technical/Functional Skills: 6-7 years experience across cloud, infrastructure engineering, production operations, platform engineering, SRE, DevOps, or enterprise technology transformation. At least 1-2 years in architecture, program ownership, or technology partner roles with accountability for critical platforms, budgets, vendors, regulatory commitments, and distributed teams. Strong expertise across Microsoft Azure and AWS, including enterprise landing zones, identity, networking, compute, storage, databases, containers, security, observability, resilience, governance, and cost management. Preferred DevOps engineering background with credible hands-on experience in CI/CD, GitOps, Infrastructure as Code, automation, scripting, containers, orchestration, observability, release engineering, and production support. Proven leadership of cloud adoption, data-center modernization, hybrid-cloud transformation, migration, platform engineering, resilience, automation, technical-debt reduction, or operating-model change. Deep knowledge of network and infrastructure architecture, identity and access management, backup and recovery, capacity, performance, virtualization, middleware, databases, patching, vulnerability management, and lifecycle governance. Demonstrated command of incident, problem, change, release, availability, configuration, continuity, disaster recovery, major-incident, and service-level management. Strong understanding of DevSecOps, zero trust, cloud security controls, operational resilience, audit evidence, technology risk, compliance, and third-party governance in banking or financial services. Excellent executive communication, stakeholder influence, vendor and commercial management, negotiation, financial management, facilitation, and organizational leadership skills. Ability to work onsite as required, lead across time zones, and provide executive and technical direction during critical incidents or planned changes. Significant experience in banking, financial services, payments, lending, deposits, risk, treasury, digital banking, or another highly regulated and high-availability environment. Demonstrated delivery of enterprise AI/ML, generative AI, agentic AI, AIOps, MLOps, or AI-enabled engineering and operations initiatives. Experience integrating Azure AI Foundry/Azure OpenAI, Azure Machine Learning, Amazon Bedrock, Amazon SageMaker, or comparable enterprise AI services into secure platforms and operating workflows. Knowledge of responsible AI, model governance, data privacy, AI evaluation, retrieval-augmented generation, prompt and model lifecycle management, human oversight, and production AI monitoring. Experience applying AI to anomaly detection, predictive incident management, event correlation, root-cause analysis, capacity forecasting, operational knowledge, engineering productivity, and automated remediation. Relevant certifications such as Azure Solutions Architect Expert, Azure DevOps Engineer Expert, AWS Solutions Architect Professional, AWS DevOps Engineer Professional, Kubernetes, Terraform, ITIL, SRE, FinOps, or cloud security credentials. Experience with ServiceNow, Splunk, Dynatrace, AppDynamics, Grafana, Prometheus, Azure Monitor, Amazon CloudWatch, OpenTelemetry, or comparable observability and service-management platforms. Experience establishing internal developer platforms, cloud centers of excellence, reusable engineering patterns, service catalogs, golden paths, and communities of practice. Roles & Responsibilities: Cloud Strategy & Architecture: Define and execute the bank's Azure and AWS strategy, target architecture, landing zones, account/subscription model, guardrails, shared services, and migration roadmap. Design secure, resilient, scalable, and cost-effective hybrid and multi-cloud solutions across compute, storage, networking, databases, integration, identity, containers, and serverless services. Lead workload assessments and select appropriate rehost, replatform, refactor, retire, retain, or replace strategies. Establish reusable architecture patterns, golden paths, reference implementations, policy-as-code, and technical standards for regulated workloads. Conduct architecture reviews and ensure alignment with enterprise architecture, security, data, operational resilience, and regulatory requirements. Infrastructure, Operations & Resilience: Own the strategic direction for hybrid infrastructure, including network, compute, storage, virtualization, identity, backup, database platforms, middleware, end-user dependencies, and data-center integrations. Improve operational maturity across incident, problem, change, release, capacity, availability, configuration, patch, vulnerability, backup, and service-continuity management. Define service-level objectives, reliability indicators, error budgets, recovery objectives, operational readiness criteria, escalation paths, and executive service reporting. Lead major incident response, root-cause analysis, corrective actions, resilience testing, disaster-recovery exercises, and systemic risk reduction. Implement end-to-end observability, event correlation, actionable alerting, service mapping, capacity forecasting, and automated remediation. Drive simplification, standardization, technical-debt reduction, lifecycle management, platform currency, and retirement of unsupported technologies. DevOps, Platform Engineering & Automation Champion a DevOps engineering culture based on shared ownership, secure automation, continuous improvement, rapid feedback, and frequent low-risk releases. Design and govern CI/CD and GitOps practices using Azure DevOps, GitHub Actions, GitLab CI, Jenkins, or equivalent enterprise tooling. Drive Infrastructure as Code and configuration automation using Terraform, Bicep/ARM, CloudFormation, Ansible, PowerShell, Python, or Bash. Lead container and orchestration adoption using Docker, Kubernetes, AKS, EKS, and related platform services. Embed automated testing, code quality, secrets management, vulnerability scanning, artifact controls, policy checks, segregation of duties, and deployment approvals into pipelines. Track engineering outcomes through deployment frequency, lead time for change, change failure rate, recovery time, availability, automation coverage, platform adoption, and technical-debt reduction. AI Integration & Intelligent Operations - Preferred: Identify and prioritize responsible AI, generative AI, and machine-learning opportunities across IT operations, engineering productivity, service management, knowledge management, fraud/risk support, and customer or employee experiences. Integrate AI capabilities using services such as Azure AI Foundry/Azure OpenAI, Azure Machine Learning, Amazon Bedrock, Amazon SageMaker, or comparable enterprise platforms. Support MLOps and GenAIOps practices, including model and prompt versioning, automated pipe lines, evaluation, deployment, observability, drift detection, grounding, rollback, and lifecycle controls. Apply secure and responsible AI principles covering privacy, data lineage, access control, human oversight, output validation, explainability, model risk, prompt-injection defense, and confidential-data protection. Evaluate AIOps use cases for anomaly detection, predictive incident management, alert reduction, root-cause analysis, capacity forecasting, and self-healing operations. Partner with data, cybersecurity, legal, compliance, model-risk, and business teams to move AI proofs of value into governed, production-ready services. Security, Risk, Compliance & FinOps: Partner with cybersecurity, enterprise architecture, operational risk, audit, compliance, data governance, and model-risk teams to implement controls aligned with bank policy and applicable regulatory expectations. Apply zero-trust, least-privilege, segregation-of-duties, encryption, key management, privileged-access, secure configuration, immutable logging, and continuous-monitoring principles. Maintain evidence-ready controls and support risk assessments, technology control testing, audits, remediation plans, regulatory examinations, and third-party reviews. Implement FinOps practices for budgeting, tagging, showback/chargeback, commitment management, rightsizing, waste reduction, licensing optimization, and unit-cost transparency. Manage vendor performance, statements of work, service levels, architectural compliance, delivery risks, commercial outcomes, and knowledge transfer.
Job Description Emerald Resource Group RPA TEAM LEAD Salary: up to $180,000, depending on background and experience. Unfortunately, only U.S. citizens will be considered for this position. ABOUT THE ROLE Our established logistics and transportation organization is investing in AI and intelligent automation. As RPA Team Lead, you will build and scale Microsoft Copilot Studio agents, establish enterprise AI governance, and take ownership of our existing UiPath RPA portfolio. Technologies include Copilot Studio, Power Platform, UiPath, and generative/agentic AI tools. KEY RESPONSIBILITIES Set the automation vision, roadmap, and quarterly OKRs; manage the intake backlog and prioritize business demand. Lead design and deployment of Copilot Studio agents for Finance, Logistics, Safety, Fleet Services, and shared services. Govern generative and agentic AI tools, and lead vendor evaluation, licensing, and build-vs-buy decisions. Take operational ownership of the UiPath portfolio as it transitions from external support to in-house, covering the full RPA lifecycle, code reviews, CI/CD, and integrations (ERP, TMS, APIs, Azure, SQL Server). Lead a Senior and a Junior Automation Engineer using Agile/Scrum in Azure DevOps; mentor and develop the team. Partner with business stakeholders, report on KPIs (cycle time, accuracy, ROI), and document processes and SOPs. REQUIRED QUALIFICATIONS Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent experience. 5+ years in intelligent automation/RPA, including 3+ years leading delivery across multiple business domains. Experience with Microsoft Copilot Studio and Power Platform (Power Automate, Power Apps). Working knowledge of UiPath (Studio/StudioX, Orchestrator, REFramework) or a similar RPA platform. Experience designing, deploying, or governing generative/agentic AI in an enterprise. Strong business analysis skills, plus SQL, API, and Azure integration experience. Excellent communication skills; able to explain technical concepts to non-technical audiences.
10/24/2026
Full time
Job Description Emerald Resource Group RPA TEAM LEAD Salary: up to $180,000, depending on background and experience. Unfortunately, only U.S. citizens will be considered for this position. ABOUT THE ROLE Our established logistics and transportation organization is investing in AI and intelligent automation. As RPA Team Lead, you will build and scale Microsoft Copilot Studio agents, establish enterprise AI governance, and take ownership of our existing UiPath RPA portfolio. Technologies include Copilot Studio, Power Platform, UiPath, and generative/agentic AI tools. KEY RESPONSIBILITIES Set the automation vision, roadmap, and quarterly OKRs; manage the intake backlog and prioritize business demand. Lead design and deployment of Copilot Studio agents for Finance, Logistics, Safety, Fleet Services, and shared services. Govern generative and agentic AI tools, and lead vendor evaluation, licensing, and build-vs-buy decisions. Take operational ownership of the UiPath portfolio as it transitions from external support to in-house, covering the full RPA lifecycle, code reviews, CI/CD, and integrations (ERP, TMS, APIs, Azure, SQL Server). Lead a Senior and a Junior Automation Engineer using Agile/Scrum in Azure DevOps; mentor and develop the team. Partner with business stakeholders, report on KPIs (cycle time, accuracy, ROI), and document processes and SOPs. REQUIRED QUALIFICATIONS Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent experience. 5+ years in intelligent automation/RPA, including 3+ years leading delivery across multiple business domains. Experience with Microsoft Copilot Studio and Power Platform (Power Automate, Power Apps). Working knowledge of UiPath (Studio/StudioX, Orchestrator, REFramework) or a similar RPA platform. Experience designing, deploying, or governing generative/agentic AI in an enterprise. Strong business analysis skills, plus SQL, API, and Azure integration experience. Excellent communication skills; able to explain technical concepts to non-technical audiences.
Job Description Project Description: We are looking for a hands-on BI Platform Operations Engineer to provide production operational support, administration, maintenance, and technical troubleshooting for our enterprise Business Intelligence platforms and associated AWS services. The role will primarily support Tableau and Amazon QuickSight / Amazon Quick Suite, including a custom BI-as-a-Service (BIaaS) platform built on top of Amazon QuickSight. The BIaaS platform consists of multiple backend services, RESTful APIs, AWS serverless and data services, and a web-based user interface. This is primarily a platform engineering and production support role, rather than a dashboard/report development position. The successful candidate will be comfortable troubleshooting across BI, application, API, data, AI-assisted analytics, and cloud infrastructure layers and working with engineering teams to maintain a reliable and stable production service. For Canada this role is for subcontractor. For NYC: compensation 00 UAS Gross per year and based on your interview results. Responsibilities: Production Operations & Support Provide day-to-day production operational support for Tableau, Amazon QuickSight / Quick Suite, the BI-as-a-Service platform, and its supporting AWS services. Monitor platform availability, performance, capacity, scheduled workloads, and overall service health. Investigate and resolve production incidents, user issues, failed jobs, connectivity problems, access issues, API failures, and platform errors. Perform root-cause analysis and work with engineering, application, data, and infrastructure teams to resolve recurring issues. Support incident, problem, release, and change-management processes. Develop and maintain operational runbooks, troubleshooting procedures, and support documentation. Identify opportunities to automate repetitive operational and administrative activities. Proactively identify platform health, reliability, and supportability improvements. Tableau Platform Administration Administer and maintain the enterprise Tableau Server environment. Support Tableau Server infrastructure, configuration, upgrades, patching, and platform maintenance. Manage sites, projects, users, groups, permissions, and content access. Troubleshoot extracts, refresh schedules, data-source connectivity, authentication, and performance issues. Monitor Tableau Server processes, logs, resource utilization, and platform health. Assist with platform upgrades, migrations, configuration changes, and infrastructure-related activities. Amazon QuickSight / Quick Suite Administration, Authoring & Support Provide day-to-day Amazon QuickSight / Quick Suite production support, administration, and operational troubleshooting. Support the lifecycle, administration, and troubleshooting of BI and analytics assets, including: Analyses Dashboards Datasets Data Sources Themes Folders Templates Topics and conversational BI capabilities Other QuickSight / Quick Suite assets and platform objects Troubleshoot issues related to creating, updating, publishing, sharing, and accessing analyses and dashboards. Support QuickSight authoring capabilities and assist authors with analysis, visualization, dataset, data-source, dashboard, and semantic configuration issues. Troubleshoot dataset ingestion and refresh failures, including SPICE ingestion, scheduled refreshes, and direct-query scenarios. Diagnose data-source connectivity and access issues involving Amazon Athena and other supported data sources. Support and troubleshoot themes and reusable assets, including permissions and sharing. Manage and troubleshoot users, groups, namespaces, folders, asset permissions, and access controls. troubleshoot dashboard and analysis permissions, sharing, ownership, and access issues. Understand and troubleshoot security and AWS integrations, including IAM-based access and permissions. Support and troubleshoot programmatic management of QuickSight assets using QuickSight APIs and AWS SDKs. Troubleshoot API operations involving analyses, dashboards, datasets, data sources, themes, permissions, and other supported assets. Support embedded QuickSight capabilities and troubleshoot embedding, authentication, authorization, and access-related issues where applicable. Amazon Q / Generative BI & Quick Suite Capabilities Support and develop operational familiarity with Amazon Q capabilities within QuickSight / Quick Suite, including natural-language and generative BI experiences. Support Amazon Q Topics and associated semantic configurations used to provide business context for natural-language analytics. Troubleshoot user access, permissions, data availability, semantic configuration, and other issues affecting Amazon Q experiences. Support conversational BI capabilities that allow users to ask questions of enterprise data using natural language. Support newer Quick Suite conversational and agentic experiences, including chat agents, agents, and spaces, where adopted by the platform. Understand how Spaces organize relevant data, content, context, and resources used by Quick Suite experiences. Assist with operational support and troubleshooting of Amazon Q / Quick Suite agents, including access, configuration, underlying data/content availability, and integration-related issues. Work with platform and engineering teams as Amazon Q and Quick Suite capabilities evolve and are incorporated into the BI-as-a-Service offering. BI-as-a-Service Platform Provide operational and technical support for our internally developed BI-as-a-Service platform built on Amazon QuickSight, including the AWS data, API, serverless, monitoring, and analytics services that underpin the platform. Platform & QuickSight Integration Understand the end-to-end BIaaS architecture and troubleshoot issues across APIs, backend services, AWS infrastructure, data services, QuickSight, and Quick Suite capabilities. Support integrations with Amazon QuickSight using QuickSight APIs and AWS SDKs. Troubleshoot the programmatic creation, update, deployment, sharing, and management of QuickSight assets. Troubleshoot asset provisioning, permissions, embedding, authentication, authorization, and other QuickSight integration workflows. Analyze application and AWS service logs to diagnose production issues. Work closely with platform engineering and development teams to identify defects and implement sustainable fixes. Assist with deployments, configuration changes, environment maintenance, and production releases. AWS Data & Analytics Services Support and troubleshoot Amazon Athena queries and integrations used by BI applications and reporting workloads. Understand Athena connectivity, permissions, workgroups, query execution, and common performance or operational issues. Support the AWS Glue Data Catalog, including databases, tables, metadata, schemas, partitions, and integrations with Athena and QuickSight. Troubleshoot metadata, schema, partition, and data-discovery issues involving the Glue Data Catalog. Understand and support AWS Lake Formation permissions and data-access controls. Troubleshoot access issues across Lake Formation, Glue Data Catalog, Athena, IAM, and QuickSight. Understand the end-to-end data-access path from underlying data sources through Athena and the Glue Data Catalog to QuickSight. AWS Serverless & REST API Services Support and troubleshoot AWS Lambda functions used by the BI-as-a-Service platform. Understand Lambda configuration, IAM roles, environment variables, triggers, execution failures, and timeouts. Support Amazon API Gateway endpoints integrated with Lambda and other backend services. Troubleshoot API Gateway routing, authentication/authorization, request and response handling, integration failures, and HTTP errors. Understand and troubleshoot RESTful APIs, including HTTP methods, headers, request/response payloads, authentication, authorization, and error handling. Use API testing and troubleshooting techniques to diagnose service integration issues. Understand interactions between API Gateway, Lambda functions, backend services, AWS APIs, and QuickSight APIs. Monitoring & Observability Use Amazon CloudWatch to monitor and troubleshoot the BIaaS platform and its supporting AWS services. Analyze CloudWatch Logs, metrics, dashboards, and alarms to identify and diagnose production issues. Troubleshoot Lambda executions and API Gateway requests using CloudWatch logging and metrics. Correlate events and logs across multiple services to identify the source of end-to-end transaction failures. Support and improve operational monitoring, alerting, and platform-health dashboards. Identify monitoring gaps and opportunities to improve proactive detection of production issues. Mandatory Skills Description: Required Skills & Experience Strong hands-on experience supporting enterprise BI platforms in production environments. Strong experience with Tableau Server administration and operational support. Strong hands-on experience with Amazon QuickSight administration, authoring . click apply for full job details
10/24/2026
Full time
Job Description Project Description: We are looking for a hands-on BI Platform Operations Engineer to provide production operational support, administration, maintenance, and technical troubleshooting for our enterprise Business Intelligence platforms and associated AWS services. The role will primarily support Tableau and Amazon QuickSight / Amazon Quick Suite, including a custom BI-as-a-Service (BIaaS) platform built on top of Amazon QuickSight. The BIaaS platform consists of multiple backend services, RESTful APIs, AWS serverless and data services, and a web-based user interface. This is primarily a platform engineering and production support role, rather than a dashboard/report development position. The successful candidate will be comfortable troubleshooting across BI, application, API, data, AI-assisted analytics, and cloud infrastructure layers and working with engineering teams to maintain a reliable and stable production service. For Canada this role is for subcontractor. For NYC: compensation 00 UAS Gross per year and based on your interview results. Responsibilities: Production Operations & Support Provide day-to-day production operational support for Tableau, Amazon QuickSight / Quick Suite, the BI-as-a-Service platform, and its supporting AWS services. Monitor platform availability, performance, capacity, scheduled workloads, and overall service health. Investigate and resolve production incidents, user issues, failed jobs, connectivity problems, access issues, API failures, and platform errors. Perform root-cause analysis and work with engineering, application, data, and infrastructure teams to resolve recurring issues. Support incident, problem, release, and change-management processes. Develop and maintain operational runbooks, troubleshooting procedures, and support documentation. Identify opportunities to automate repetitive operational and administrative activities. Proactively identify platform health, reliability, and supportability improvements. Tableau Platform Administration Administer and maintain the enterprise Tableau Server environment. Support Tableau Server infrastructure, configuration, upgrades, patching, and platform maintenance. Manage sites, projects, users, groups, permissions, and content access. Troubleshoot extracts, refresh schedules, data-source connectivity, authentication, and performance issues. Monitor Tableau Server processes, logs, resource utilization, and platform health. Assist with platform upgrades, migrations, configuration changes, and infrastructure-related activities. Amazon QuickSight / Quick Suite Administration, Authoring & Support Provide day-to-day Amazon QuickSight / Quick Suite production support, administration, and operational troubleshooting. Support the lifecycle, administration, and troubleshooting of BI and analytics assets, including: Analyses Dashboards Datasets Data Sources Themes Folders Templates Topics and conversational BI capabilities Other QuickSight / Quick Suite assets and platform objects Troubleshoot issues related to creating, updating, publishing, sharing, and accessing analyses and dashboards. Support QuickSight authoring capabilities and assist authors with analysis, visualization, dataset, data-source, dashboard, and semantic configuration issues. Troubleshoot dataset ingestion and refresh failures, including SPICE ingestion, scheduled refreshes, and direct-query scenarios. Diagnose data-source connectivity and access issues involving Amazon Athena and other supported data sources. Support and troubleshoot themes and reusable assets, including permissions and sharing. Manage and troubleshoot users, groups, namespaces, folders, asset permissions, and access controls. troubleshoot dashboard and analysis permissions, sharing, ownership, and access issues. Understand and troubleshoot security and AWS integrations, including IAM-based access and permissions. Support and troubleshoot programmatic management of QuickSight assets using QuickSight APIs and AWS SDKs. Troubleshoot API operations involving analyses, dashboards, datasets, data sources, themes, permissions, and other supported assets. Support embedded QuickSight capabilities and troubleshoot embedding, authentication, authorization, and access-related issues where applicable. Amazon Q / Generative BI & Quick Suite Capabilities Support and develop operational familiarity with Amazon Q capabilities within QuickSight / Quick Suite, including natural-language and generative BI experiences. Support Amazon Q Topics and associated semantic configurations used to provide business context for natural-language analytics. Troubleshoot user access, permissions, data availability, semantic configuration, and other issues affecting Amazon Q experiences. Support conversational BI capabilities that allow users to ask questions of enterprise data using natural language. Support newer Quick Suite conversational and agentic experiences, including chat agents, agents, and spaces, where adopted by the platform. Understand how Spaces organize relevant data, content, context, and resources used by Quick Suite experiences. Assist with operational support and troubleshooting of Amazon Q / Quick Suite agents, including access, configuration, underlying data/content availability, and integration-related issues. Work with platform and engineering teams as Amazon Q and Quick Suite capabilities evolve and are incorporated into the BI-as-a-Service offering. BI-as-a-Service Platform Provide operational and technical support for our internally developed BI-as-a-Service platform built on Amazon QuickSight, including the AWS data, API, serverless, monitoring, and analytics services that underpin the platform. Platform & QuickSight Integration Understand the end-to-end BIaaS architecture and troubleshoot issues across APIs, backend services, AWS infrastructure, data services, QuickSight, and Quick Suite capabilities. Support integrations with Amazon QuickSight using QuickSight APIs and AWS SDKs. Troubleshoot the programmatic creation, update, deployment, sharing, and management of QuickSight assets. Troubleshoot asset provisioning, permissions, embedding, authentication, authorization, and other QuickSight integration workflows. Analyze application and AWS service logs to diagnose production issues. Work closely with platform engineering and development teams to identify defects and implement sustainable fixes. Assist with deployments, configuration changes, environment maintenance, and production releases. AWS Data & Analytics Services Support and troubleshoot Amazon Athena queries and integrations used by BI applications and reporting workloads. Understand Athena connectivity, permissions, workgroups, query execution, and common performance or operational issues. Support the AWS Glue Data Catalog, including databases, tables, metadata, schemas, partitions, and integrations with Athena and QuickSight. Troubleshoot metadata, schema, partition, and data-discovery issues involving the Glue Data Catalog. Understand and support AWS Lake Formation permissions and data-access controls. Troubleshoot access issues across Lake Formation, Glue Data Catalog, Athena, IAM, and QuickSight. Understand the end-to-end data-access path from underlying data sources through Athena and the Glue Data Catalog to QuickSight. AWS Serverless & REST API Services Support and troubleshoot AWS Lambda functions used by the BI-as-a-Service platform. Understand Lambda configuration, IAM roles, environment variables, triggers, execution failures, and timeouts. Support Amazon API Gateway endpoints integrated with Lambda and other backend services. Troubleshoot API Gateway routing, authentication/authorization, request and response handling, integration failures, and HTTP errors. Understand and troubleshoot RESTful APIs, including HTTP methods, headers, request/response payloads, authentication, authorization, and error handling. Use API testing and troubleshooting techniques to diagnose service integration issues. Understand interactions between API Gateway, Lambda functions, backend services, AWS APIs, and QuickSight APIs. Monitoring & Observability Use Amazon CloudWatch to monitor and troubleshoot the BIaaS platform and its supporting AWS services. Analyze CloudWatch Logs, metrics, dashboards, and alarms to identify and diagnose production issues. Troubleshoot Lambda executions and API Gateway requests using CloudWatch logging and metrics. Correlate events and logs across multiple services to identify the source of end-to-end transaction failures. Support and improve operational monitoring, alerting, and platform-health dashboards. Identify monitoring gaps and opportunities to improve proactive detection of production issues. Mandatory Skills Description: Required Skills & Experience Strong hands-on experience supporting enterprise BI platforms in production environments. Strong experience with Tableau Server administration and operational support. Strong hands-on experience with Amazon QuickSight administration, authoring . click apply for full job details
Job Description Job Description FlowX.AI is an agentic AI platform for large-scale, mission-critical applications. We started in regulated industries - banking and insurance - and have successfully landed major deals in government and defense. We're now expanding into new verticals including logistics, manufacturing, healthcare, and construction. Our platform enables large organizations to deploy, run, and monitor mission-critical AI agents at scale. We provide the governance layers and robustness that AI agents need to operate reliably across the most important business processes. Mission As a Presales Engineer, your role is to contribute to creating and tailoring end-to-end solutions for our customers, using the FlowX product, making sure we are efficient and deliver in the right quality. What you will do on a daily basis Collaborate with the team to understand business requirements and configure solutions accordingly. Test and validate configurations to ensure they meet the business and functional expectations. Design and develop Proof of Concepts (POCs), working closely with senior engineers to validate feasibility and effectiveness. Maintain comprehensive and up-to-date configuration documentation. Stay updated with the latest trends, tools, and technologies. Recommend enhancements or optimizations based on feedback. You're very likely to succeed if you have 3+ year of experience as a Configuration Engineer, QA Engineer, Software Engineer, or in a similar technical role. Bachelor degree in Computer Science, Business Information or a related field. Familiarity with Python. Basic knowledge of HTML, CSS and Angular concepts. Knowledge in any BPMN platforms (e.g. Appian, Camunda, IBM, Pega) is a plus. You believe there is always room for improvement and you want to constantly keep learning. You consider that good cooperation with colleagues is very important. What's in it for you Direct impact on how the company scales and delivers multi-million-dollar enterprise transformation deals. Competitive salary range of $150-200k based on experience. Early-stage equity package. Work alongside an ambitious leadership team driving category creation in enterprise AI. Collaborative, fast-paced culture with room to grow and make an impact. Location: New York City Benefits Private Health Insurance Paid Time Off Training & Development
10/24/2026
Full time
Job Description Job Description FlowX.AI is an agentic AI platform for large-scale, mission-critical applications. We started in regulated industries - banking and insurance - and have successfully landed major deals in government and defense. We're now expanding into new verticals including logistics, manufacturing, healthcare, and construction. Our platform enables large organizations to deploy, run, and monitor mission-critical AI agents at scale. We provide the governance layers and robustness that AI agents need to operate reliably across the most important business processes. Mission As a Presales Engineer, your role is to contribute to creating and tailoring end-to-end solutions for our customers, using the FlowX product, making sure we are efficient and deliver in the right quality. What you will do on a daily basis Collaborate with the team to understand business requirements and configure solutions accordingly. Test and validate configurations to ensure they meet the business and functional expectations. Design and develop Proof of Concepts (POCs), working closely with senior engineers to validate feasibility and effectiveness. Maintain comprehensive and up-to-date configuration documentation. Stay updated with the latest trends, tools, and technologies. Recommend enhancements or optimizations based on feedback. You're very likely to succeed if you have 3+ year of experience as a Configuration Engineer, QA Engineer, Software Engineer, or in a similar technical role. Bachelor degree in Computer Science, Business Information or a related field. Familiarity with Python. Basic knowledge of HTML, CSS and Angular concepts. Knowledge in any BPMN platforms (e.g. Appian, Camunda, IBM, Pega) is a plus. You believe there is always room for improvement and you want to constantly keep learning. You consider that good cooperation with colleagues is very important. What's in it for you Direct impact on how the company scales and delivers multi-million-dollar enterprise transformation deals. Competitive salary range of $150-200k based on experience. Early-stage equity package. Work alongside an ambitious leadership team driving category creation in enterprise AI. Collaborative, fast-paced culture with room to grow and make an impact. Location: New York City Benefits Private Health Insurance Paid Time Off Training & Development
Job Description Job Description About GOARC GOARC is an AI-native B2B SaaS company helping industrial organizations protect their people, improve frontline execution, and reduce operational risk. Our platform brings intelligence, automation, and real-time visibility to some of the most complex and high-stakes operating environments in the world. GOARC helps companies move beyond reactive safety and operations management toward a smarter, more predictive, AI-driven way of working. The market opportunity is massive. Industrial companies are under pressure to modernize, improve safety performance, reduce risk, and operate with greater speed and precision. GOARC is built for that moment. We have a clear industry-leading offering, a wide-open market, and a leadership team with a proven track record of building category-defining companies. GOARC's CEO and SVP of Revenue Operations have helped build two companies valued at more than $1 billion in this category. For the right person, this is a rare opportunity to get in early, learn directly from proven leaders, and help build the next great AI-native industrial SaaS company. The Opportunity GoARC has strong demand and seasoned leadership that is personally running demand gen and outbound. What's missing is the system underneath: the data, the automations, the content infrastructure, the AI workflows that turn a sophisticated sales motion into a compounding revenue machine. That's the job. You are part GTM strategist, part technical backbone of our revenue engine. You will design, ship, and continuously improve the systems, automations, and AI-powered workflows that help our small, sharp go-to-market team operate at a level that looks impossible on paper. You will build the infrastructure that lets us scale without proportional headcount. This is a high-visibility, high-ownership founding role. You will own the full go-to-market tech stack. You will work side by side with the CEO and head of sales. You will turn go-to-market from a set of disconnected tools into a cohesive, intelligent system. What you'll build in your first 6 to 12 months • The trigger-moment content engine. Structured intake of customer stories, transcripts, and field notes, transformed into an LLM-readable repo that powers personalized outreach, case studies, and account-specific assets at scale. • The data and enrichment layer. ICP definition, data enrichment orchestration, intent and signal pipes (LinkedIn, news, hiring triggers, M&A), prospect list automation. The system the rest of GTM runs on. • The outbound and follow-on comms stack. LinkedIn (automated and CEO-amplified), email sequencing, multi-touch nurture, AI personalization layered over named-account lists. • The attribution and pipeline review. Dashboards the CEO can run in fifteen minutes a week. Source-to-revenue visibility. The truth, not vanity metrics. • The internal AI workflow library. Claude Code, n8n, Make, or a similar iPaaS or agentic coding tool, MCPs and APIs wired into our stack so the team gets leverage out of every contact, every signal, every customer conversation. Day to day A morning might be writing a Clay table that enriches a fresh list of plant-level operations VPs across 200 process-industry sites, wiring it into a sequenced campaign in Smartlead, and routing replies to the right person automatically. The afternoon might be sitting with the CEO turning a 45-minute customer call recording into three field-ready content assets and a templated case-study draft. The next day you might be debugging a Salesforce-to-HubSpot sync, or shipping a Claude-powered agent that drafts personalized LinkedIn messages off real-time hiring signals, or rebuilding the pipeline dashboard so the weekly review takes 20 minutes instead of two hours. You are hands-on every day. You ship systems. You experiment quickly. You solve messy real-world problems with software, AI, and judgment. Requirements Must-haves • Self-starter. You see what needs to be built and you build it without being asked. • 2 to 5 years in early stage B2B startups, ideally as one of the first GTM-side hires. • Hands-on experience leveraging AI and LLMs in real workflows - Claude, OpenAI, agentic tools like Claude Code, not slideware. • Comfortable with API integrations and basic SQL / data querying. • Required familiarity with the modern GTM stack: Clay, Apollo, HubSpot or Salesforce, LinkedIn Sales Navigator, an outbound tool (Smartlead, Instantly, or equivalent), a workflow tool (n8n, Make, or Gumloop), and/or agentic coding tools like Claude Code, Codex, or Gemini CLI. These are not bonuses. You cannot do this job without them. • Technical, but not a software engineer. You wire systems together, write the SQL, hit the APIs, build the automations. The job is not to ship product code or sit in sprint planning. • Has taste to write well enough to draft sequences, content, and briefs without a copywriter standing by. • Comfortable working directly with a professional, hands-on CEO. • Bias to build. You'd rather ship one rough automation today than design three perfect ones for next quarter. Nice to have • Background in industrial software, EHS, operations, compliance, or OT - or a track record of getting up to speed fast in unfamiliar industrial buyer contexts. • You've built a customer-evidence repo from raw transcripts and stories before. • You've wired LLM workflows into a real revenue motion that drove pipeline. • You've managed outbound email and messaging at scale. Not the right fit if • You're a software engineer curious about a GTM-adjacent role but you have no experience with GTM tools. • You're a VP Marketing operator who manages rather than builds. • You're a pure strategist. Execution capacity is what we need. • You're a demand-gen specialist whose strength is optimizing inbound funnels. We don't have inbound yet. How we work and what you get • Founding-team GTM scope, equity, and proximity to the CEO. • The first in-house GTM hire at a company with marquee enterprise customers, fresh capital, and a 10-year head start on the product. • An operating model built for this era: AI tokens and lightweight workflow tools replace classic seat-heavy Mar/Sales Tech. Your stack will look different from the average GTM team's. Higher variable usage on AI and automation. Lower fixed SaaS seat spend. More leverage per dollar. Benefits Competitive compensation Comprehensive medical, dental, and vision coverage 401(k) retirement savings plan Flexible paid time off and company holidays Professional development and industry conference opportunities Remote-first work environment with flexibility
10/24/2026
Full time
Job Description Job Description About GOARC GOARC is an AI-native B2B SaaS company helping industrial organizations protect their people, improve frontline execution, and reduce operational risk. Our platform brings intelligence, automation, and real-time visibility to some of the most complex and high-stakes operating environments in the world. GOARC helps companies move beyond reactive safety and operations management toward a smarter, more predictive, AI-driven way of working. The market opportunity is massive. Industrial companies are under pressure to modernize, improve safety performance, reduce risk, and operate with greater speed and precision. GOARC is built for that moment. We have a clear industry-leading offering, a wide-open market, and a leadership team with a proven track record of building category-defining companies. GOARC's CEO and SVP of Revenue Operations have helped build two companies valued at more than $1 billion in this category. For the right person, this is a rare opportunity to get in early, learn directly from proven leaders, and help build the next great AI-native industrial SaaS company. The Opportunity GoARC has strong demand and seasoned leadership that is personally running demand gen and outbound. What's missing is the system underneath: the data, the automations, the content infrastructure, the AI workflows that turn a sophisticated sales motion into a compounding revenue machine. That's the job. You are part GTM strategist, part technical backbone of our revenue engine. You will design, ship, and continuously improve the systems, automations, and AI-powered workflows that help our small, sharp go-to-market team operate at a level that looks impossible on paper. You will build the infrastructure that lets us scale without proportional headcount. This is a high-visibility, high-ownership founding role. You will own the full go-to-market tech stack. You will work side by side with the CEO and head of sales. You will turn go-to-market from a set of disconnected tools into a cohesive, intelligent system. What you'll build in your first 6 to 12 months • The trigger-moment content engine. Structured intake of customer stories, transcripts, and field notes, transformed into an LLM-readable repo that powers personalized outreach, case studies, and account-specific assets at scale. • The data and enrichment layer. ICP definition, data enrichment orchestration, intent and signal pipes (LinkedIn, news, hiring triggers, M&A), prospect list automation. The system the rest of GTM runs on. • The outbound and follow-on comms stack. LinkedIn (automated and CEO-amplified), email sequencing, multi-touch nurture, AI personalization layered over named-account lists. • The attribution and pipeline review. Dashboards the CEO can run in fifteen minutes a week. Source-to-revenue visibility. The truth, not vanity metrics. • The internal AI workflow library. Claude Code, n8n, Make, or a similar iPaaS or agentic coding tool, MCPs and APIs wired into our stack so the team gets leverage out of every contact, every signal, every customer conversation. Day to day A morning might be writing a Clay table that enriches a fresh list of plant-level operations VPs across 200 process-industry sites, wiring it into a sequenced campaign in Smartlead, and routing replies to the right person automatically. The afternoon might be sitting with the CEO turning a 45-minute customer call recording into three field-ready content assets and a templated case-study draft. The next day you might be debugging a Salesforce-to-HubSpot sync, or shipping a Claude-powered agent that drafts personalized LinkedIn messages off real-time hiring signals, or rebuilding the pipeline dashboard so the weekly review takes 20 minutes instead of two hours. You are hands-on every day. You ship systems. You experiment quickly. You solve messy real-world problems with software, AI, and judgment. Requirements Must-haves • Self-starter. You see what needs to be built and you build it without being asked. • 2 to 5 years in early stage B2B startups, ideally as one of the first GTM-side hires. • Hands-on experience leveraging AI and LLMs in real workflows - Claude, OpenAI, agentic tools like Claude Code, not slideware. • Comfortable with API integrations and basic SQL / data querying. • Required familiarity with the modern GTM stack: Clay, Apollo, HubSpot or Salesforce, LinkedIn Sales Navigator, an outbound tool (Smartlead, Instantly, or equivalent), a workflow tool (n8n, Make, or Gumloop), and/or agentic coding tools like Claude Code, Codex, or Gemini CLI. These are not bonuses. You cannot do this job without them. • Technical, but not a software engineer. You wire systems together, write the SQL, hit the APIs, build the automations. The job is not to ship product code or sit in sprint planning. • Has taste to write well enough to draft sequences, content, and briefs without a copywriter standing by. • Comfortable working directly with a professional, hands-on CEO. • Bias to build. You'd rather ship one rough automation today than design three perfect ones for next quarter. Nice to have • Background in industrial software, EHS, operations, compliance, or OT - or a track record of getting up to speed fast in unfamiliar industrial buyer contexts. • You've built a customer-evidence repo from raw transcripts and stories before. • You've wired LLM workflows into a real revenue motion that drove pipeline. • You've managed outbound email and messaging at scale. Not the right fit if • You're a software engineer curious about a GTM-adjacent role but you have no experience with GTM tools. • You're a VP Marketing operator who manages rather than builds. • You're a pure strategist. Execution capacity is what we need. • You're a demand-gen specialist whose strength is optimizing inbound funnels. We don't have inbound yet. How we work and what you get • Founding-team GTM scope, equity, and proximity to the CEO. • The first in-house GTM hire at a company with marquee enterprise customers, fresh capital, and a 10-year head start on the product. • An operating model built for this era: AI tokens and lightweight workflow tools replace classic seat-heavy Mar/Sales Tech. Your stack will look different from the average GTM team's. Higher variable usage on AI and automation. Lower fixed SaaS seat spend. More leverage per dollar. Benefits Competitive compensation Comprehensive medical, dental, and vision coverage 401(k) retirement savings plan Flexible paid time off and company holidays Professional development and industry conference opportunities Remote-first work environment with flexibility
CONFISA INTERNATIONAL GROUP
Concord, North Carolina
Job Description Job Description: Job Description Role: Forward Deployed Engineer (FDE) - Agentic AI , Americas Role Type: Client-facing, hands-on engineering + solutioning (pre-sales through early delivery) Location: Major US hub (e.g., New York, Bay Area, Dallas, Chicago - flexible for the right candidate). Travel Expected. Level: Manager/Principal/Senior Principal Business Unit: (Enterprise AI Platforms & Agentic Solutions). Reporting to: Artificial Intelligence Leader, North America. About Company: Global technology consulting and digital solutions company that enables enterprises across industries to reimagine how they operate, compete, and grow. By combining deep domain knowledge, advanced engineering, and transformation expertise, delivers measurable business impact at scale. Enterprise AI business unit, purpose built to help organizations move beyond pilots into scaled, product iongrade AI adoption, functional consulting, an AI Foundry, and agentic AI platforms to deliver integrated solutions that transform how work gets done across the enterprise.Operating across Banking, Insurance, Manufacturing, Retail/CPG, Energy & Utilities, Travel & Hospitality, HiTech, and Media, works closely with industry GTM teams to create repeatable AI solutions, accelerators, and transformation programs aligned to real business outcomes. Role summary Building and scaling enterprise grade GenAI + Agentic AI capabilities (Foundry, orchestration, governance/AgentOp, accelerators, and verticalized agents) across Fortune 2000 clients. As a Forward Deployed Engineer (FDE), you will operate at the intersection of executive problem framing, rapid engineering, and scalable architecture. You will engage directly with client CXOs and ssseni or leaders to identify a highvalue wedge, build a working proof in weeks, and translate that into a clear path toward MVP and scaled rollout on or nay hyper-scaler platforms. You will be embedded in deals and client journeys-shipping real agentic systems, not slides. Key Responsibilities 1) Executive-facing discovery & value shaping Partner with Sales, Client Partners, and leaders to run CXO-level working sessions that translate business pain into an Outcome Thesis (KPI tree, baseline, uplift hypothesis, operating constraints). Identify the right "thin-slice" wedge for a rapid Proof of Value (PoV): high signal, measurable, low dependency, enterprise-relevant. Communicate trade-offs clearly: what to automate vs assist, where human-in-the-loop is required, and what "safe scale" entails. 2) Rapid Proof of Value engineering Build a rapid PoV that demonstrates business impact + technical feasibility trustworthiness Implement agent workflowswith tooluse, integration to enterprise systems, and lightweight UI/experience for stakeholder clarity. Create initial evaluation harness (task success, accuracy, policy compliance, tool-call success, latency/cost) and capture baseline vs PoV lift. 3) Agentic blueprint & path-to-scale handoff Produce a scalable Agentic Blueprint: agent roles, orchestration patterns, tools/APIs, data/knowledge strategy, guardrails, observability, and HIL escalation. Define MVP backlog and architecture decisions: identity/access, audit, logging, fallback design, policy boundaries, and deployment model. Hand off cleanly to delivery teams (MVP squads / pods) with a production-ready plan. 4) Solution architecture + client ecosystem Design solutions using constructs or hyper scaler Agentic AI foundations (Foundry, knowledge fabric/RAG patterns, agent orchestration, governance, observability/AgentOps Deploy across hyperscalers (AWS/Azure/GCP) and/or client environments; integrate with enterprise apps (e.g., ServiceNow, Salesforce, SAP, Microsoft stack). Ensure alignment with enterprise security, privacy, and compliance requirements. 5) Deal acceleration & differentiation Serve as a technical "closer" in competitive pursuits: demos, workshops, architecture deep dives, and technical validation. Create reusable accelerators: prompt packs, workflow templates, connectors, eval suites, reference architectures-so future PoVs become faster and repeatable. Contribute to thought leadership and field playbooks (industry use cases, POV patterns, autonomy ladder constructs, commercial hooks). What success looks like (12-month outcomes) Time-to-first-value: PoVs that reliably land in weeks with measurable outcomes. Conversion: Strong PoV to MVP conversion rate (and clear roadmap to scale). Executive confidence: CXO trust built through crisp narratives + working systems. Reusable assets: Field-ready accelerators that reduce PoV cycle time quarter over quarter. Scalability: PoVs designed with production realities in mind (security, observability, cost, controls). Required Qualifications 8+ years of experience in full-stack engineering / solution architecture / technical consulting, with significant client-facing delivery. Demonstrated ability to build quickly under ambiguity and still maintain sound architecture instincts. Strong experience with modern AI stacks, including: LLMs / agent patterns: orchestration, tool-calling, planning/execution loops, memory patterns RAG & knowledge: embedding strategies, chunking, vector databases, retrieval evaluation Evaluation: offline + online evals, regression suites, safety tests/red-teaming basics Strong software engineering fundamentals: APIs, data pipelines, integration, distributed systems basics. Proficiency in Python + one of (TypeScript/Java/Go); comfortable across frontend + backend as needed. Cloud-native experience (AWS/Azure/GCP), containers, CI/CD, and observability fundamentals. Exceptional communication: can translate technical complexity into business impact for senior stakeholders. Preferred Qualifications Proven delivery of GenAI/agentic solutions in production (not only prototypes). Experience integrating with enterprise platforms: ServiceNow, Salesforce, SAP, Microsoft (Graph/O365), Databricks/Snowflake, etc. Experience with security & governance for GenAI: data boundaries, access control, auditability, model risk, compliance evidence. Familiarity with LLMOps/AgentOps concepts: versioning, telemetry, drift monitoring, cost governance (FinOps). Domain depth in one or more focus verticals (Travel/Transportation/Hospitality, Retail/CPG, BFSI, Manufacturing, Media). Relevant certifications (cloud, security, data) are a plus. Core competencies (what distinguishes top performers) Executive presence: credible with CXOs; can run a room and drive decisions. Structured problem framing: quickly identifies the highest-leverage wedge and measurement plan. Builder mindset: ships working systems rapidly; demos are grounded in real integrations. Judgment & trust: knows where agents must be constrained; designs for safe autonomy. Repeatability: turns one-off wins into reusable playbooks and accelerators. Working model Travel: expected (client workshops, PoVs, exec meetings). Operates in tight pods with Sales, Industry leaders, BlueVerse product/platform teams, and delivery squads. Strong bias toward hands-on building (this role is not purely advisory). Benefits Comprehensive Medical Plan Covering Medical, Dental, Vision Short Term and Long-Term Disability Coverage 401(k) Plan with Company match Life Insurance Vacation Time, Sick Leave, Paid Holidays Paid Paternity and Maternity Leave Additional Information All your information will be kept confidential according to EEO guidelines.
10/24/2026
Full time
Job Description Job Description: Job Description Role: Forward Deployed Engineer (FDE) - Agentic AI , Americas Role Type: Client-facing, hands-on engineering + solutioning (pre-sales through early delivery) Location: Major US hub (e.g., New York, Bay Area, Dallas, Chicago - flexible for the right candidate). Travel Expected. Level: Manager/Principal/Senior Principal Business Unit: (Enterprise AI Platforms & Agentic Solutions). Reporting to: Artificial Intelligence Leader, North America. About Company: Global technology consulting and digital solutions company that enables enterprises across industries to reimagine how they operate, compete, and grow. By combining deep domain knowledge, advanced engineering, and transformation expertise, delivers measurable business impact at scale. Enterprise AI business unit, purpose built to help organizations move beyond pilots into scaled, product iongrade AI adoption, functional consulting, an AI Foundry, and agentic AI platforms to deliver integrated solutions that transform how work gets done across the enterprise.Operating across Banking, Insurance, Manufacturing, Retail/CPG, Energy & Utilities, Travel & Hospitality, HiTech, and Media, works closely with industry GTM teams to create repeatable AI solutions, accelerators, and transformation programs aligned to real business outcomes. Role summary Building and scaling enterprise grade GenAI + Agentic AI capabilities (Foundry, orchestration, governance/AgentOp, accelerators, and verticalized agents) across Fortune 2000 clients. As a Forward Deployed Engineer (FDE), you will operate at the intersection of executive problem framing, rapid engineering, and scalable architecture. You will engage directly with client CXOs and ssseni or leaders to identify a highvalue wedge, build a working proof in weeks, and translate that into a clear path toward MVP and scaled rollout on or nay hyper-scaler platforms. You will be embedded in deals and client journeys-shipping real agentic systems, not slides. Key Responsibilities 1) Executive-facing discovery & value shaping Partner with Sales, Client Partners, and leaders to run CXO-level working sessions that translate business pain into an Outcome Thesis (KPI tree, baseline, uplift hypothesis, operating constraints). Identify the right "thin-slice" wedge for a rapid Proof of Value (PoV): high signal, measurable, low dependency, enterprise-relevant. Communicate trade-offs clearly: what to automate vs assist, where human-in-the-loop is required, and what "safe scale" entails. 2) Rapid Proof of Value engineering Build a rapid PoV that demonstrates business impact + technical feasibility trustworthiness Implement agent workflowswith tooluse, integration to enterprise systems, and lightweight UI/experience for stakeholder clarity. Create initial evaluation harness (task success, accuracy, policy compliance, tool-call success, latency/cost) and capture baseline vs PoV lift. 3) Agentic blueprint & path-to-scale handoff Produce a scalable Agentic Blueprint: agent roles, orchestration patterns, tools/APIs, data/knowledge strategy, guardrails, observability, and HIL escalation. Define MVP backlog and architecture decisions: identity/access, audit, logging, fallback design, policy boundaries, and deployment model. Hand off cleanly to delivery teams (MVP squads / pods) with a production-ready plan. 4) Solution architecture + client ecosystem Design solutions using constructs or hyper scaler Agentic AI foundations (Foundry, knowledge fabric/RAG patterns, agent orchestration, governance, observability/AgentOps Deploy across hyperscalers (AWS/Azure/GCP) and/or client environments; integrate with enterprise apps (e.g., ServiceNow, Salesforce, SAP, Microsoft stack). Ensure alignment with enterprise security, privacy, and compliance requirements. 5) Deal acceleration & differentiation Serve as a technical "closer" in competitive pursuits: demos, workshops, architecture deep dives, and technical validation. Create reusable accelerators: prompt packs, workflow templates, connectors, eval suites, reference architectures-so future PoVs become faster and repeatable. Contribute to thought leadership and field playbooks (industry use cases, POV patterns, autonomy ladder constructs, commercial hooks). What success looks like (12-month outcomes) Time-to-first-value: PoVs that reliably land in weeks with measurable outcomes. Conversion: Strong PoV to MVP conversion rate (and clear roadmap to scale). Executive confidence: CXO trust built through crisp narratives + working systems. Reusable assets: Field-ready accelerators that reduce PoV cycle time quarter over quarter. Scalability: PoVs designed with production realities in mind (security, observability, cost, controls). Required Qualifications 8+ years of experience in full-stack engineering / solution architecture / technical consulting, with significant client-facing delivery. Demonstrated ability to build quickly under ambiguity and still maintain sound architecture instincts. Strong experience with modern AI stacks, including: LLMs / agent patterns: orchestration, tool-calling, planning/execution loops, memory patterns RAG & knowledge: embedding strategies, chunking, vector databases, retrieval evaluation Evaluation: offline + online evals, regression suites, safety tests/red-teaming basics Strong software engineering fundamentals: APIs, data pipelines, integration, distributed systems basics. Proficiency in Python + one of (TypeScript/Java/Go); comfortable across frontend + backend as needed. Cloud-native experience (AWS/Azure/GCP), containers, CI/CD, and observability fundamentals. Exceptional communication: can translate technical complexity into business impact for senior stakeholders. Preferred Qualifications Proven delivery of GenAI/agentic solutions in production (not only prototypes). Experience integrating with enterprise platforms: ServiceNow, Salesforce, SAP, Microsoft (Graph/O365), Databricks/Snowflake, etc. Experience with security & governance for GenAI: data boundaries, access control, auditability, model risk, compliance evidence. Familiarity with LLMOps/AgentOps concepts: versioning, telemetry, drift monitoring, cost governance (FinOps). Domain depth in one or more focus verticals (Travel/Transportation/Hospitality, Retail/CPG, BFSI, Manufacturing, Media). Relevant certifications (cloud, security, data) are a plus. Core competencies (what distinguishes top performers) Executive presence: credible with CXOs; can run a room and drive decisions. Structured problem framing: quickly identifies the highest-leverage wedge and measurement plan. Builder mindset: ships working systems rapidly; demos are grounded in real integrations. Judgment & trust: knows where agents must be constrained; designs for safe autonomy. Repeatability: turns one-off wins into reusable playbooks and accelerators. Working model Travel: expected (client workshops, PoVs, exec meetings). Operates in tight pods with Sales, Industry leaders, BlueVerse product/platform teams, and delivery squads. Strong bias toward hands-on building (this role is not purely advisory). Benefits Comprehensive Medical Plan Covering Medical, Dental, Vision Short Term and Long-Term Disability Coverage 401(k) Plan with Company match Life Insurance Vacation Time, Sick Leave, Paid Holidays Paid Paternity and Maternity Leave Additional Information All your information will be kept confidential according to EEO guidelines.
Job Description Job Description Founding Applied AI Engineer - Proactive AI Agents Location: San Francisco, CA Work Arrangement: Onsite, 5 days/week Employment Type: Full-time Base Salary: 300K-500K Equity: Substantial founding-level equity Visa: Open to visa transfers and new sponsorship Relocation: Relocation support available About the Opportunity An early-stage, research-first AI company is building the next generation of personal, proactive AI agents. Rather than creating another chatbot or copilot that waits for instructions, the team is developing agents that learn how people work, understand intent, anticipate needs, and autonomously pursue objectives on their behalf. The product observes the context naturally created during work-including screen activity, conversations, and actions across tools-to understand what users are trying to accomplish and how they make decisions. It uses those signals to automate repetitive workflows and proactively identify opportunities to advance users' goals. This is a ground-floor opportunity to join a three-person founding team and help shape the product, technical architecture, and engineering culture from the beginning. The Role We're looking for a Founding Applied AI Engineer who has already built and shipped AI-powered products hands-on. You'll own 01 product areas end-to-end, working across user-facing products, agent workflows, model orchestration, and infrastructure. This is a high-agency environment. You'll be expected to make important technical and product decisions independently, move quickly, work closely with users, and take responsibility for outcomes rather than simply implementing predefined specifications. What You'll Do Own and ship zero-to-one AI product areas end-to-end Build and iterate on agent harnesses and agentic systems that observe user behavior, learn intent, and automate workflows Work across user-facing interfaces, agent workflows, models, orchestration, and infrastructure Make independent technical and product decisions around system design, reliability, and shipping speed Work directly with users and enterprise customers to understand workflows Rapidly prototype and iterate on solutions Help shape product vision, technical architecture, and company culture What We're Looking For Core requirements Experience at an AI lab, applied AI startup, or infrastructure company Hands-on experience owning and shipping a 01 ML/AI model-based product or agentic system to production Hands-on experience building agentic systems, agent harnesses, or model-based AI/ML products Deep professional expertise with Python Ability to operate across product, engineering, models, and infrastructure Ability to contribute to product vision, ideation, and feature definition Ability to work onsite in San Francisco 5 days per week The target profile has 2-6 years of experience as an Applied AI Engineer . Strong Additional Signals Early-stage startup engineering experience Founder experience Early-hire experience at a high-growth AI startup Major end-to-end product ownership Strong academic background from a top-tier global institution Tech Stack Python, LLMs, agent frameworks, model orchestration, production ML systems. Working Environment This is a small, high-intensity founding environment built around extreme ownership, speed, high agency, and close collaboration. Engineers operate across research and product development rather than staying inside narrowly defined functions. Team members are expected to make strong technical and product decisions independently and help define what gets built. The team works together five days per week in San Francisco . Compensation & Equity 300K- 500K base salary + substantial founding-level equity. Compensation is benchmarked to candidate market value rather than years of experience alone. Visa & Relocation The company is open to: Visa transfers, including OPT and H-1B transfers New sponsorship, including H-1B and TN Relocation for candidates outside San Francisco Client Interview Process (after candidates are submitted to the client) Stage 1 - CEO Intro Call 30 minutes Virtual Conversation covering your background, motivation, mission alignment, interest in the company's vision, and what you're looking for. Stage 2 - Technical Interview 45-60 minutes Virtual Depending on your background, this may include system design, a deep dive into a feature or system you've owned, or a broader technical conversation. The team will evaluate hands-on technical depth, engineering judgment, and understanding of AI/agent systems. Stage 3 - Onsite Final 3-4 hours San Francisco A half-day onsite including conversations with the team, a work trial or collaborative exercise, and an opportunity to experience the working environment.
10/24/2026
Full time
Job Description Job Description Founding Applied AI Engineer - Proactive AI Agents Location: San Francisco, CA Work Arrangement: Onsite, 5 days/week Employment Type: Full-time Base Salary: 300K-500K Equity: Substantial founding-level equity Visa: Open to visa transfers and new sponsorship Relocation: Relocation support available About the Opportunity An early-stage, research-first AI company is building the next generation of personal, proactive AI agents. Rather than creating another chatbot or copilot that waits for instructions, the team is developing agents that learn how people work, understand intent, anticipate needs, and autonomously pursue objectives on their behalf. The product observes the context naturally created during work-including screen activity, conversations, and actions across tools-to understand what users are trying to accomplish and how they make decisions. It uses those signals to automate repetitive workflows and proactively identify opportunities to advance users' goals. This is a ground-floor opportunity to join a three-person founding team and help shape the product, technical architecture, and engineering culture from the beginning. The Role We're looking for a Founding Applied AI Engineer who has already built and shipped AI-powered products hands-on. You'll own 01 product areas end-to-end, working across user-facing products, agent workflows, model orchestration, and infrastructure. This is a high-agency environment. You'll be expected to make important technical and product decisions independently, move quickly, work closely with users, and take responsibility for outcomes rather than simply implementing predefined specifications. What You'll Do Own and ship zero-to-one AI product areas end-to-end Build and iterate on agent harnesses and agentic systems that observe user behavior, learn intent, and automate workflows Work across user-facing interfaces, agent workflows, models, orchestration, and infrastructure Make independent technical and product decisions around system design, reliability, and shipping speed Work directly with users and enterprise customers to understand workflows Rapidly prototype and iterate on solutions Help shape product vision, technical architecture, and company culture What We're Looking For Core requirements Experience at an AI lab, applied AI startup, or infrastructure company Hands-on experience owning and shipping a 01 ML/AI model-based product or agentic system to production Hands-on experience building agentic systems, agent harnesses, or model-based AI/ML products Deep professional expertise with Python Ability to operate across product, engineering, models, and infrastructure Ability to contribute to product vision, ideation, and feature definition Ability to work onsite in San Francisco 5 days per week The target profile has 2-6 years of experience as an Applied AI Engineer . Strong Additional Signals Early-stage startup engineering experience Founder experience Early-hire experience at a high-growth AI startup Major end-to-end product ownership Strong academic background from a top-tier global institution Tech Stack Python, LLMs, agent frameworks, model orchestration, production ML systems. Working Environment This is a small, high-intensity founding environment built around extreme ownership, speed, high agency, and close collaboration. Engineers operate across research and product development rather than staying inside narrowly defined functions. Team members are expected to make strong technical and product decisions independently and help define what gets built. The team works together five days per week in San Francisco . Compensation & Equity 300K- 500K base salary + substantial founding-level equity. Compensation is benchmarked to candidate market value rather than years of experience alone. Visa & Relocation The company is open to: Visa transfers, including OPT and H-1B transfers New sponsorship, including H-1B and TN Relocation for candidates outside San Francisco Client Interview Process (after candidates are submitted to the client) Stage 1 - CEO Intro Call 30 minutes Virtual Conversation covering your background, motivation, mission alignment, interest in the company's vision, and what you're looking for. Stage 2 - Technical Interview 45-60 minutes Virtual Depending on your background, this may include system design, a deep dive into a feature or system you've owned, or a broader technical conversation. The team will evaluate hands-on technical depth, engineering judgment, and understanding of AI/agent systems. Stage 3 - Onsite Final 3-4 hours San Francisco A half-day onsite including conversations with the team, a work trial or collaborative exercise, and an opportunity to experience the working environment.
Job Description Job Description Role Overview You've spent years learning to code, build systems, solve technical problems. Now use that analytical mindset in a new way. As our Technical Marketing Specialist, you will: - Deeply understand our AI and Agentic solutions (technical depth matters here) - Explain complex tech concepts to non-technical buyers (articulate what engineers built) - Create technical content: case studies, technical blogs, architecture diagrams, demos - Work directly with our engineers to understand product capabilities - Help sales teams explain technical value to enterprise customers - Measure what works and iterate based on data What You'll Own Technical Translation & Content Translate engineering specifications into buyer-friendly language Write technical blog posts explaining AI/Agentic concepts Create case studies with technical depth AND business outcomes Build technical diagrams, architecture explanations, how-it-works content Answer complex technical questions from prospects Participate in technical demos and sales calls Campaign Support & Execution Help execute technical campaigns targeting engineers at buyer companies Support webinars with technical content (code examples, architecture, deep dives) Create technical comparison content (our solution vs. competitors) Support technical Q&A content and FAQs Track engagement on technical content (data-driven) Research & Analysis Research how enterprises use AI/Agentic solutions Analyze competitor technical offerings and positioning Study industry trends in AI/automation/ML Collect customer feedback on technical capabilities Document product capabilities for sales/marketing use Sales Enablement Create materials that help sales engineers close deals Develop technical FAQs and response templates Build technical battlecards (how to position against competitors) Support pre-sales technical discussions Create technical value proposition documents Why This Is Perfect For Engineers Use Your Technical Skills: Code understanding, system thinking, analytical rigor-all valued here Data-Driven: Everything measured. What content works? What messaging converts? Prove it with data. Problem-Solving: How do you explain a complex distributed system to a CEO? How do you position automation vs. human labor? Solve these communication problems. No BS Nonsense: We don't do vanity metrics ("10K impressions!"). We care about business outcomes. Engineers respect that. Learn Business: See how your technical work drives leads, pipeline, revenue. Understand the business side of technology. Credibility: Talk to engineers and technical decision-makers as a peer. You speak their language. Career Path: Technical marketing is a legitimate career (not a fallback). Step into this, become an expert, position for product management or director roles. Requirements Computer Science degree OR engineering degree (any discipline) Strong analytical and logical thinking Comfortable with technical complexity (don't need to build it, but must understand it) Attention to detail and precision (marketing copy for technical products must be exact) Can explain complex systems in writing (clear, structured, logical) Curious about how things work Data-driven mindset (measure what works) Communication Skills Can write clearly and precisely (not creative, but accurate) Can explain technical concepts to non-technical people Strong communication (written more than verbal-this is technical content) Can diagram systems and explain architecture Attention to detail in writing Nice-to-Have Technical Skill Python, JavaScript, or any programming language Understanding of APIs and system integration Knowledge of AI/ML/automation concepts Database or SQL experience Git, GitHub, or version control Linux/command line basics Cloud platforms (AWS, GCP, Azure) Understanding of enterprise software architecture Integrant is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment. We do not discriminate on the basis of race, color, religion, national origin, sex, age, disability, sexual orientation, gender identity, genetic information, or any other protected characteristic. Benefits Salary : $50,000-$65,000 annually - Based on education, technical background, communication skills - SB 642 compliant (California transparency)
10/24/2026
Full time
Job Description Job Description Role Overview You've spent years learning to code, build systems, solve technical problems. Now use that analytical mindset in a new way. As our Technical Marketing Specialist, you will: - Deeply understand our AI and Agentic solutions (technical depth matters here) - Explain complex tech concepts to non-technical buyers (articulate what engineers built) - Create technical content: case studies, technical blogs, architecture diagrams, demos - Work directly with our engineers to understand product capabilities - Help sales teams explain technical value to enterprise customers - Measure what works and iterate based on data What You'll Own Technical Translation & Content Translate engineering specifications into buyer-friendly language Write technical blog posts explaining AI/Agentic concepts Create case studies with technical depth AND business outcomes Build technical diagrams, architecture explanations, how-it-works content Answer complex technical questions from prospects Participate in technical demos and sales calls Campaign Support & Execution Help execute technical campaigns targeting engineers at buyer companies Support webinars with technical content (code examples, architecture, deep dives) Create technical comparison content (our solution vs. competitors) Support technical Q&A content and FAQs Track engagement on technical content (data-driven) Research & Analysis Research how enterprises use AI/Agentic solutions Analyze competitor technical offerings and positioning Study industry trends in AI/automation/ML Collect customer feedback on technical capabilities Document product capabilities for sales/marketing use Sales Enablement Create materials that help sales engineers close deals Develop technical FAQs and response templates Build technical battlecards (how to position against competitors) Support pre-sales technical discussions Create technical value proposition documents Why This Is Perfect For Engineers Use Your Technical Skills: Code understanding, system thinking, analytical rigor-all valued here Data-Driven: Everything measured. What content works? What messaging converts? Prove it with data. Problem-Solving: How do you explain a complex distributed system to a CEO? How do you position automation vs. human labor? Solve these communication problems. No BS Nonsense: We don't do vanity metrics ("10K impressions!"). We care about business outcomes. Engineers respect that. Learn Business: See how your technical work drives leads, pipeline, revenue. Understand the business side of technology. Credibility: Talk to engineers and technical decision-makers as a peer. You speak their language. Career Path: Technical marketing is a legitimate career (not a fallback). Step into this, become an expert, position for product management or director roles. Requirements Computer Science degree OR engineering degree (any discipline) Strong analytical and logical thinking Comfortable with technical complexity (don't need to build it, but must understand it) Attention to detail and precision (marketing copy for technical products must be exact) Can explain complex systems in writing (clear, structured, logical) Curious about how things work Data-driven mindset (measure what works) Communication Skills Can write clearly and precisely (not creative, but accurate) Can explain technical concepts to non-technical people Strong communication (written more than verbal-this is technical content) Can diagram systems and explain architecture Attention to detail in writing Nice-to-Have Technical Skill Python, JavaScript, or any programming language Understanding of APIs and system integration Knowledge of AI/ML/automation concepts Database or SQL experience Git, GitHub, or version control Linux/command line basics Cloud platforms (AWS, GCP, Azure) Understanding of enterprise software architecture Integrant is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment. We do not discriminate on the basis of race, color, religion, national origin, sex, age, disability, sexual orientation, gender identity, genetic information, or any other protected characteristic. Benefits Salary : $50,000-$65,000 annually - Based on education, technical background, communication skills - SB 642 compliant (California transparency)
Job Description Job Description Top-of-market comp. $180K$300K base plus meaningful founding-team equity at a 30-person company that's just getting started. VERY FAST INTERVIEW PROCESS and WILLINGNESS TO MOVE QUICK Open to SWE, FDE, Product Eng, Implementations Eng with VC-backed pedigree who has experience scaling enterprise deployments Extreme ownership from day one. You work directly with enterprise customers and ship end-to-end AI automations that show up immediately in their workflows no bureaucracy, no hand-holding. Why candidates should join Rocket-ship traction.Client hit multiple seven figures in ARR in just over a year and already serves 30% of the top 50 insurance brokers in the country. Fresh $25M Series A.Backed by CRV, South Park Commons and Foundation Capital strong institutional names with deep enterprise software conviction. Extreme ownership from day one.You work directly with enterprise customers and ship end-to-end AI automations that show up immediately in their workflows no bureaucracy, no hand-holding. Top-of-market comp.$180K$300K base plus meaningful founding-team equity at a 30-person company that's just getting started. Staff-level team pedigree.You're joining ex-Affirm, Uber, DoorDash and McKinsey operators in-person in San Francisco the kind of team that moves fast and builds things that matter. Dealbreaker Work experience Customer-facing engineering experience on post-sales sidescoping problems, building solutions, and shipping within the same week VC-backed startup pedigree experience at a company that raised Series A+ from a top-tier VC Hard skills Proficient in Python and/or TypeScript Baseline Seniority 3 - 6 years of experience in software engineering, forward deployed, solutions, implementation or product engineering role is 70% coding (mix of customer-facing builds and platform work) and 30% customer-facing scoping and requirements gathering Work experience Experience scaling enterprise deployments building and shipping multiple product features or automations for large customers Education Degree from a top university Ivies, Stanford, MIT, Berkeley, Duke, Harvey Mudd, Claremont McKenna, UIUC, or equivalent strongly preferred Soft skills Comfortable context-switching between customer-facing communication and heads-down coding Miscellaneous On-site 5 days/week in San Francisco Work experience Built or deployed AI/ML automations or agentic workflows in a production environment Hard skills Experience building or integrating LLM/AI automation pipelines (e.g. OpenAI, LangChain, or similar) Traits to avoid Candidates with only large-company experience, no startup exposure or software consulting backgrounds SWEs-turned-FDEs looking to return to pure SWE (no direct customer interaction experience) Job hoppers 3+ jobs in 3 years for early-career candidates is a red flag International candidates without substantive US work experience About this role This is a hands-on, customer-facing engineering role at the intersection of AI, product, and implementation. You'll work directly with some of the largest insurance brokerages in the countryunderstanding their workflows, building AI automations on Client's platform, and shipping solutions within days, not months. The role is 70% coding and 30% customer-facing scoping. You'll hop on a call with a customer to understand their problem, then build and deploy an agent that solves itoften in the same week. Everything you build feeds back into the platform, so your work scales beyond a single customer. You won't be doing pre-sales, demos, or upsellingdeployment strategists handle that. Your job is to deeply understand the business domain and build. This role is ideal for engineers who thrive at the intersection of customer empathy and technical execution, and who want to work on cutting-edge AI agents transforming a $1.5 trillion industry that hasn't seen real technology in 20 years. What You Will Do Own end-to-end implementation of enterprise customer engagementsfrom requirements gathering to deployed solution Build and ship AI agents and automations on Client's platform, deepening adoption within our largest strategic accounts Work directly with enterprise customers (post-sales) to understand business problems and scope technical solutions Ship rapidlyexpect to deliver working solutions within the same week as customer conversations Integrate successful customer-specific work back into Client's core platform Collaborate with product and engineering teams to translate customer insights into scalable features About You 36 years of post-college engineering experience, no new grads Customer-facing engineering experiencecomfortable interfacing with enterprise customers regularly Full-stack proficiencycomfortable shipping across front end and back end Experience at a VC-backed startup (Series A+) funded by top-tier VCs Degree from a top university (Ivies, Stanford, MIT, Berkeley, Duke, Harvey Mudd, UIUC, or equivalent) Strong ownership mentalityyou don't wait for process, you create it Bonus Qualifications Experience building or deploying AI/ML automations or agentic workflows in production Proficiency in Python and/or TypeScript Experience in insurance or financial services domain Salary $180K - $300K On-site work policy 5 days in-office in SoMa, San Francisco, CA Full-time position Location SoMa, San Francisco, CA Tech stack Python, TypeScript, AI/ML Automations Equity Competitive equity Visa sponsorship details Open to visa transfers(e.g. OPT, H1B transfers) Hiring Count Looking to hire 2 - 3candidates for this role
10/24/2026
Full time
Job Description Job Description Top-of-market comp. $180K$300K base plus meaningful founding-team equity at a 30-person company that's just getting started. VERY FAST INTERVIEW PROCESS and WILLINGNESS TO MOVE QUICK Open to SWE, FDE, Product Eng, Implementations Eng with VC-backed pedigree who has experience scaling enterprise deployments Extreme ownership from day one. You work directly with enterprise customers and ship end-to-end AI automations that show up immediately in their workflows no bureaucracy, no hand-holding. Why candidates should join Rocket-ship traction.Client hit multiple seven figures in ARR in just over a year and already serves 30% of the top 50 insurance brokers in the country. Fresh $25M Series A.Backed by CRV, South Park Commons and Foundation Capital strong institutional names with deep enterprise software conviction. Extreme ownership from day one.You work directly with enterprise customers and ship end-to-end AI automations that show up immediately in their workflows no bureaucracy, no hand-holding. Top-of-market comp.$180K$300K base plus meaningful founding-team equity at a 30-person company that's just getting started. Staff-level team pedigree.You're joining ex-Affirm, Uber, DoorDash and McKinsey operators in-person in San Francisco the kind of team that moves fast and builds things that matter. Dealbreaker Work experience Customer-facing engineering experience on post-sales sidescoping problems, building solutions, and shipping within the same week VC-backed startup pedigree experience at a company that raised Series A+ from a top-tier VC Hard skills Proficient in Python and/or TypeScript Baseline Seniority 3 - 6 years of experience in software engineering, forward deployed, solutions, implementation or product engineering role is 70% coding (mix of customer-facing builds and platform work) and 30% customer-facing scoping and requirements gathering Work experience Experience scaling enterprise deployments building and shipping multiple product features or automations for large customers Education Degree from a top university Ivies, Stanford, MIT, Berkeley, Duke, Harvey Mudd, Claremont McKenna, UIUC, or equivalent strongly preferred Soft skills Comfortable context-switching between customer-facing communication and heads-down coding Miscellaneous On-site 5 days/week in San Francisco Work experience Built or deployed AI/ML automations or agentic workflows in a production environment Hard skills Experience building or integrating LLM/AI automation pipelines (e.g. OpenAI, LangChain, or similar) Traits to avoid Candidates with only large-company experience, no startup exposure or software consulting backgrounds SWEs-turned-FDEs looking to return to pure SWE (no direct customer interaction experience) Job hoppers 3+ jobs in 3 years for early-career candidates is a red flag International candidates without substantive US work experience About this role This is a hands-on, customer-facing engineering role at the intersection of AI, product, and implementation. You'll work directly with some of the largest insurance brokerages in the countryunderstanding their workflows, building AI automations on Client's platform, and shipping solutions within days, not months. The role is 70% coding and 30% customer-facing scoping. You'll hop on a call with a customer to understand their problem, then build and deploy an agent that solves itoften in the same week. Everything you build feeds back into the platform, so your work scales beyond a single customer. You won't be doing pre-sales, demos, or upsellingdeployment strategists handle that. Your job is to deeply understand the business domain and build. This role is ideal for engineers who thrive at the intersection of customer empathy and technical execution, and who want to work on cutting-edge AI agents transforming a $1.5 trillion industry that hasn't seen real technology in 20 years. What You Will Do Own end-to-end implementation of enterprise customer engagementsfrom requirements gathering to deployed solution Build and ship AI agents and automations on Client's platform, deepening adoption within our largest strategic accounts Work directly with enterprise customers (post-sales) to understand business problems and scope technical solutions Ship rapidlyexpect to deliver working solutions within the same week as customer conversations Integrate successful customer-specific work back into Client's core platform Collaborate with product and engineering teams to translate customer insights into scalable features About You 36 years of post-college engineering experience, no new grads Customer-facing engineering experiencecomfortable interfacing with enterprise customers regularly Full-stack proficiencycomfortable shipping across front end and back end Experience at a VC-backed startup (Series A+) funded by top-tier VCs Degree from a top university (Ivies, Stanford, MIT, Berkeley, Duke, Harvey Mudd, UIUC, or equivalent) Strong ownership mentalityyou don't wait for process, you create it Bonus Qualifications Experience building or deploying AI/ML automations or agentic workflows in production Proficiency in Python and/or TypeScript Experience in insurance or financial services domain Salary $180K - $300K On-site work policy 5 days in-office in SoMa, San Francisco, CA Full-time position Location SoMa, San Francisco, CA Tech stack Python, TypeScript, AI/ML Automations Equity Competitive equity Visa sponsorship details Open to visa transfers(e.g. OPT, H1B transfers) Hiring Count Looking to hire 2 - 3candidates for this role
Job Description Job Description Why Keyrus, Why Now! Keyrus is an international group of 3,000 experts across 30 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does. For more than 30 years, we have helped organizations build the data foundations that power intelligent systems. Today, we go further by designing and operationalizing intelligence that delivers measurable business value. Building AI capabilities is only part of the challenge. Real transformation happens when those capabilities work within existing business systems, data environments, workflows, and processes. At Keyrus, you'll work closely with clients to turn AI opportunities into production-ready solutions, connecting engineering expertise with real business needs. Technology amplifies. Keyrus culture differentiates. Industrial discipline connects the two. Role details • Location: United States (Remote) • Contract: Permanent, full-time What you'll architect As a Forward Deployed AI Engineer, you'll work directly with clients to take AI solutions from technical discovery through implementation, deployment, and adoption. You'll combine hands-on software engineering, enterprise integrations, cloud technologies, and AI capabilities to deliver solutions that work in real client environments. Your focus goes beyond writing code. You'll understand client challenges, define implementation plans, coordinate technical teams, and deliver measurable business results. Your responsibilities • Lead technical discovery with clients to understand their systems, data, workflows, and integration requirements. • Translate business needs and approved architectures into practical implementation plans. • Develop production-ready solutions integrating APIs, enterprise applications, cloud services, data platforms, and AI technologies. • Collaborate with AI Engineers, Architects, and Data Engineers to resolve technical challenges and deliver integrated solutions. • Own technical deployment and rollout within the agreed scope, ensuring reliable production performance. • Work closely with client teams to troubleshoot issues, support adoption, and improve implemented solutions. • Monitor performance, costs, and business outcomes while documenting reusable engineering practices. • Turn successful implementations into reusable Keyrus assets, including integration patterns, deployment practices, technical documentation, and implementation guidance for future engagements. Who you are You are a hands-on engineer with a strong consulting mindset who enjoys solving complex business and technical challenges. You develop production-quality code, understand enterprise integrations, and adapt solutions to real-world constraints. You are comfortable working directly with clients, managing evolving requirements, and taking ownership of technical delivery. You combine strong engineering skills with excellent communication, problem-solving, and collaboration. What you bring Experience • 5+ years of professional experience in software engineering, cloud engineering, enterprise integration, or related technical delivery roles. • Hands-on experience developing, integrating, and deploying production software or AI-enabled solutions. • Experience building and maintaining backend services, APIs, and enterprise integrations. • Experience working with cloud platforms, deployment processes, and production environments. • Ability to translate business requirements into technical implementation plans. • Strong client-facing consulting experience, including communicating technical decisions and working with business stakeholders. Technical skills • Strong programming skills in Python, TypeScript/JavaScript, Java, C#, or similar languages. • Experience with REST APIs, backend services, authentication, and enterprise integrations. • Hands-on experience with AWS, Azure, or Google Cloud. • Knowledge of Git, CI/CD, testing, containers, and production monitoring. • Familiarity with LLM APIs, RAG, AI agents, and GenAI application integration. • Understanding of application security, system reliability, and cloud deployment practices. Nice to have • Experience developing production GenAI applications or agentic workflows. • Familiarity with LangGraph, MCP, OpenAI, Anthropic, or similar technologies. • Experience with Kubernetes, Docker, or infrastructure as code. • Background in backend, full-stack, data, or cloud engineering. • Experience delivering solutions in regulated or complex enterprise environments. • Previous experience in consulting, professional services, or client-embedded engineering roles. What makes you successful • You turn complex challenges into practical solutions. • You take ownership of technical delivery and integration. • You communicate clearly and collaborate effectively with clients and teams. • You deliver reliable solutions that create measurable business value. WHAT WE OFFER (KUS) A stimulating environment where you will be able to surpass yourself and discover new horizons. A strong culture of innovation and entrepreneurship. Health insurance for you and your family members. 401K plan. Monthly Wellness Allowance. 4 weeks of paid vacation; Language courses (French & English) Access to continuing education (in-house, conferences, events, courses, certifications, etc.) The expected base compensation for this position ranges from $135,000 to $155,000 USD, depending on experience, skills, location, and internal equity. This salary range is provided as a guideline and may be adjusted for the selected candidate. About Keyrus At Keyrus, we help organizations move from experimental to industrialized AI, from isolated agents to orchestrated systems, and from insight to execution. As Architects of Intelligence, we design the Operating System of the intelligent enterprise, embedding intelligence into business processes so that it creates sustainable, measurable value. Powered by our proprietary Human Orchestrated Model (HOM), we architect reliable: • Intelligence Foundations • Human in Command governance • Performance Steering With over 30 years of expertise and 3,000 experts across 30 countries, we build adaptive, resilient intelligent environments where technology amplifies human capability and performance compounds over time. AI does not transform businesses. Architected intelligence does. For more information:
10/24/2026
Full time
Job Description Job Description Why Keyrus, Why Now! Keyrus is an international group of 3,000 experts across 30 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does. For more than 30 years, we have helped organizations build the data foundations that power intelligent systems. Today, we go further by designing and operationalizing intelligence that delivers measurable business value. Building AI capabilities is only part of the challenge. Real transformation happens when those capabilities work within existing business systems, data environments, workflows, and processes. At Keyrus, you'll work closely with clients to turn AI opportunities into production-ready solutions, connecting engineering expertise with real business needs. Technology amplifies. Keyrus culture differentiates. Industrial discipline connects the two. Role details • Location: United States (Remote) • Contract: Permanent, full-time What you'll architect As a Forward Deployed AI Engineer, you'll work directly with clients to take AI solutions from technical discovery through implementation, deployment, and adoption. You'll combine hands-on software engineering, enterprise integrations, cloud technologies, and AI capabilities to deliver solutions that work in real client environments. Your focus goes beyond writing code. You'll understand client challenges, define implementation plans, coordinate technical teams, and deliver measurable business results. Your responsibilities • Lead technical discovery with clients to understand their systems, data, workflows, and integration requirements. • Translate business needs and approved architectures into practical implementation plans. • Develop production-ready solutions integrating APIs, enterprise applications, cloud services, data platforms, and AI technologies. • Collaborate with AI Engineers, Architects, and Data Engineers to resolve technical challenges and deliver integrated solutions. • Own technical deployment and rollout within the agreed scope, ensuring reliable production performance. • Work closely with client teams to troubleshoot issues, support adoption, and improve implemented solutions. • Monitor performance, costs, and business outcomes while documenting reusable engineering practices. • Turn successful implementations into reusable Keyrus assets, including integration patterns, deployment practices, technical documentation, and implementation guidance for future engagements. Who you are You are a hands-on engineer with a strong consulting mindset who enjoys solving complex business and technical challenges. You develop production-quality code, understand enterprise integrations, and adapt solutions to real-world constraints. You are comfortable working directly with clients, managing evolving requirements, and taking ownership of technical delivery. You combine strong engineering skills with excellent communication, problem-solving, and collaboration. What you bring Experience • 5+ years of professional experience in software engineering, cloud engineering, enterprise integration, or related technical delivery roles. • Hands-on experience developing, integrating, and deploying production software or AI-enabled solutions. • Experience building and maintaining backend services, APIs, and enterprise integrations. • Experience working with cloud platforms, deployment processes, and production environments. • Ability to translate business requirements into technical implementation plans. • Strong client-facing consulting experience, including communicating technical decisions and working with business stakeholders. Technical skills • Strong programming skills in Python, TypeScript/JavaScript, Java, C#, or similar languages. • Experience with REST APIs, backend services, authentication, and enterprise integrations. • Hands-on experience with AWS, Azure, or Google Cloud. • Knowledge of Git, CI/CD, testing, containers, and production monitoring. • Familiarity with LLM APIs, RAG, AI agents, and GenAI application integration. • Understanding of application security, system reliability, and cloud deployment practices. Nice to have • Experience developing production GenAI applications or agentic workflows. • Familiarity with LangGraph, MCP, OpenAI, Anthropic, or similar technologies. • Experience with Kubernetes, Docker, or infrastructure as code. • Background in backend, full-stack, data, or cloud engineering. • Experience delivering solutions in regulated or complex enterprise environments. • Previous experience in consulting, professional services, or client-embedded engineering roles. What makes you successful • You turn complex challenges into practical solutions. • You take ownership of technical delivery and integration. • You communicate clearly and collaborate effectively with clients and teams. • You deliver reliable solutions that create measurable business value. WHAT WE OFFER (KUS) A stimulating environment where you will be able to surpass yourself and discover new horizons. A strong culture of innovation and entrepreneurship. Health insurance for you and your family members. 401K plan. Monthly Wellness Allowance. 4 weeks of paid vacation; Language courses (French & English) Access to continuing education (in-house, conferences, events, courses, certifications, etc.) The expected base compensation for this position ranges from $135,000 to $155,000 USD, depending on experience, skills, location, and internal equity. This salary range is provided as a guideline and may be adjusted for the selected candidate. About Keyrus At Keyrus, we help organizations move from experimental to industrialized AI, from isolated agents to orchestrated systems, and from insight to execution. As Architects of Intelligence, we design the Operating System of the intelligent enterprise, embedding intelligence into business processes so that it creates sustainable, measurable value. Powered by our proprietary Human Orchestrated Model (HOM), we architect reliable: • Intelligence Foundations • Human in Command governance • Performance Steering With over 30 years of expertise and 3,000 experts across 30 countries, we build adaptive, resilient intelligent environments where technology amplifies human capability and performance compounds over time. AI does not transform businesses. Architected intelligence does. For more information:
Job Description Job Description About the Role Join an early-stage industrial AI team as its first engineering hire after the founders. You will work directly with enterprise field teams to turn real-world technician workflows and undocumented knowledge into useful AI-powered tools for critical infrastructure. What You'll Do Work alongside field technicians to identify process and knowledge gaps, then structure information for AI applications. Build practical integrations and retrieval-augmented generation pipelines connecting customer systems such as ServiceNow, SAP, Salesforce, and learning management platforms. Create evaluations using real-world data and partner with engineering to improve AI models and features. Own customer deployments from late-stage demonstrations and onboarding through production rollout and ongoing success. Share field feedback with the product team and support adoption and change management in large organizations. What We're Looking For One to five years of experience in forward-deployed, solutions, or implementation engineering, with hands-on software development and customer-facing deployment work. Experience deploying software for enterprise, industrial, energy, or defense customers, and taking deployments into production with measurable outcomes. Hands-on experience building integrations or data pipelines across multiple systems using APIs, webhooks, ETL, or enterprise platforms such as ServiceNow, SAP, ERP, or CMMS. Experience shipping LLM or RAG features into customer workflows and building evaluations to assess their performance. Technical degree or equivalent demonstrated record of shipping deployments; strong modern AI skills, including agentic or retrieval-based systems. Comfort working independently across customer delivery and early-stage product needs, and willingness to travel domestically about 25% of the time. Founding or early-stage experience with responsibility for customer deployments and commercial outcomes is valuable. Compensation & Benefits Base salary: $160,000 to $245,000 annually. Visa sponsorship is available. Location On-site in San Francisco, United States.
10/24/2026
Full time
Job Description Job Description About the Role Join an early-stage industrial AI team as its first engineering hire after the founders. You will work directly with enterprise field teams to turn real-world technician workflows and undocumented knowledge into useful AI-powered tools for critical infrastructure. What You'll Do Work alongside field technicians to identify process and knowledge gaps, then structure information for AI applications. Build practical integrations and retrieval-augmented generation pipelines connecting customer systems such as ServiceNow, SAP, Salesforce, and learning management platforms. Create evaluations using real-world data and partner with engineering to improve AI models and features. Own customer deployments from late-stage demonstrations and onboarding through production rollout and ongoing success. Share field feedback with the product team and support adoption and change management in large organizations. What We're Looking For One to five years of experience in forward-deployed, solutions, or implementation engineering, with hands-on software development and customer-facing deployment work. Experience deploying software for enterprise, industrial, energy, or defense customers, and taking deployments into production with measurable outcomes. Hands-on experience building integrations or data pipelines across multiple systems using APIs, webhooks, ETL, or enterprise platforms such as ServiceNow, SAP, ERP, or CMMS. Experience shipping LLM or RAG features into customer workflows and building evaluations to assess their performance. Technical degree or equivalent demonstrated record of shipping deployments; strong modern AI skills, including agentic or retrieval-based systems. Comfort working independently across customer delivery and early-stage product needs, and willingness to travel domestically about 25% of the time. Founding or early-stage experience with responsibility for customer deployments and commercial outcomes is valuable. Compensation & Benefits Base salary: $160,000 to $245,000 annually. Visa sponsorship is available. Location On-site in San Francisco, United States.
Job Description Job Description Hybrid - Berkeley, CA Assignment: 10/26/2026 - 10/27/2027 $80/hr Role Summary As a Site Reliability Engineer on the Operations Technology team, you'll be part of a round-the-clock crew keeping a national-scale HPC facility accessible, reliable, and secure. Working from advanced monitoring and data collection systems, you'll proactively catch issues before they escalate, triage and resolve alerts across compute, storage, and network systems, and build the automation that makes the whole environment more resilient over time. You'll also collaborate closely with cross-functional teams to coordinate maintenance, improve tooling, and ensure the infrastructure scales smoothly as demand grows, keeping the computational power behind critical scientific research running without interruption. What You'll Own Monitor and triage alerts across computer, storage, network, and facility systems in real time Build automation that prevents issues before they become outages Develop new tools and integrations across the monitoring pipeline (APIs alerts action) Walk the data center floor to keep power, cooling, and environmental systems humming Coordinate maintenance activities across teams and keep incidents accurately tracked Dig into complex, ambiguous problems and drive them to resolution What You Bring Comfort working Owl shift (12am-8am) , 5 days/week, hybrid onsite in Berkeley, CA Solid Linux/command-line (SSH) chops Programming/scripting experience - Python, C, C++, Perl, or Java A self-starter mindset - eager to pick up Kubernetes, Prometheus/VictoriaMetrics, Alertmanager, and building management/cooling systems Network security fundamentals (ACLs, firewalls) Strong cross-team communication and collaboration skills Nice to Have Experience building or deploying Agentic AI / autonomous automation for technical workflows ServiceNow implementation experience ITSM best-practice know-how Company Description Great Organization! Company Description Great Organization!
10/24/2026
Full time
Job Description Job Description Hybrid - Berkeley, CA Assignment: 10/26/2026 - 10/27/2027 $80/hr Role Summary As a Site Reliability Engineer on the Operations Technology team, you'll be part of a round-the-clock crew keeping a national-scale HPC facility accessible, reliable, and secure. Working from advanced monitoring and data collection systems, you'll proactively catch issues before they escalate, triage and resolve alerts across compute, storage, and network systems, and build the automation that makes the whole environment more resilient over time. You'll also collaborate closely with cross-functional teams to coordinate maintenance, improve tooling, and ensure the infrastructure scales smoothly as demand grows, keeping the computational power behind critical scientific research running without interruption. What You'll Own Monitor and triage alerts across computer, storage, network, and facility systems in real time Build automation that prevents issues before they become outages Develop new tools and integrations across the monitoring pipeline (APIs alerts action) Walk the data center floor to keep power, cooling, and environmental systems humming Coordinate maintenance activities across teams and keep incidents accurately tracked Dig into complex, ambiguous problems and drive them to resolution What You Bring Comfort working Owl shift (12am-8am) , 5 days/week, hybrid onsite in Berkeley, CA Solid Linux/command-line (SSH) chops Programming/scripting experience - Python, C, C++, Perl, or Java A self-starter mindset - eager to pick up Kubernetes, Prometheus/VictoriaMetrics, Alertmanager, and building management/cooling systems Network security fundamentals (ACLs, firewalls) Strong cross-team communication and collaboration skills Nice to Have Experience building or deploying Agentic AI / autonomous automation for technical workflows ServiceNow implementation experience ITSM best-practice know-how Company Description Great Organization! Company Description Great Organization!
Qaurs Techno Systems LLC
Charlotte, North Carolina
Job Description Job Description Experience: 5 + Years AI Engineer Job Summary: We are seeking an innovative and highly skilled AI Engineer to join our dynamic team. The ideal candidate will bridge the gap between traditional software engineering and cutting-edge artificial intelligence. You will be instrumental in designing, building, and deploying advanced AI agents, working closely with Large Language Models (LLMs), and driving automated code generation initiatives. If you have a strong foundation in Java and Python, coupled with hands-on experience using Google's AI tools, we want you to help us build the next generation of intelligent applications. Key Responsibilities: AI Agent Development: Design, build, and deploy autonomous AI agents capable of reasoning, planning, and executing complex workflows. LLM Integration: Integrate cutting-edge Large Language Models (LLMs) into our core products and services to enhance functionality and user experience. Model Context Protocol (MCP) Implementation: Utilize the Model Context Protocol (MCP) to securely connect our AI models to various data sources, tools, and development environments. Automated Code Generation: Leverage AI and LLMs to build systems that assist in, or fully automate, code generation, testing, and optimization processes. System Engineering: Write clean, scalable, and maintainable code in both Java and Python to support AI backend infrastructure. Google Ecosystem Integration: Utilize Google ADK (AI Developer Kits) and related Google Cloud AI services (e.g., Vertex AI, Gemini APIs) to deploy robust AI solutions. Cross-Functional Collaboration: Work closely with product managers, data scientists, and frontend engineers to translate business requirements into technical AI solutions. Must-Have Qualifications: Programming Languages: Strong proficiency in both Java and Python, with a proven track record of building production-grade software. Google AI Tools: Hands-on experience with Google ADK (or equivalent Google Cloud AI/Vertex AI tools). LLM Expertise: Deep comfort level and practical experience working with Large Language Models (prompt engineering, fine-tuning, RAG architectures). Agentic Workflows: Demonstrable experience in building and orchestrating AI Agents (using frameworks like LangChain, LangGraph, or custom implementations). MCP Knowledge: Familiarity and practical experience with the Model Context Protocol (MCP) for standardizing AI interactions with external tools. Code Generation: Experience in leveraging AI tools or building pipelines specifically for code generation and software automation. Good-to-Have (Optional but highly valued): Experience with modern robust backend frameworks (e.g., Spring Boot for Java, FastAPI for Python). Familiarity with containerization and orchestration (Docker, Kubernetes). Experience with vector databases (e.g., Pinecone, Weaviate, Milvus).
10/24/2026
Full time
Job Description Job Description Experience: 5 + Years AI Engineer Job Summary: We are seeking an innovative and highly skilled AI Engineer to join our dynamic team. The ideal candidate will bridge the gap between traditional software engineering and cutting-edge artificial intelligence. You will be instrumental in designing, building, and deploying advanced AI agents, working closely with Large Language Models (LLMs), and driving automated code generation initiatives. If you have a strong foundation in Java and Python, coupled with hands-on experience using Google's AI tools, we want you to help us build the next generation of intelligent applications. Key Responsibilities: AI Agent Development: Design, build, and deploy autonomous AI agents capable of reasoning, planning, and executing complex workflows. LLM Integration: Integrate cutting-edge Large Language Models (LLMs) into our core products and services to enhance functionality and user experience. Model Context Protocol (MCP) Implementation: Utilize the Model Context Protocol (MCP) to securely connect our AI models to various data sources, tools, and development environments. Automated Code Generation: Leverage AI and LLMs to build systems that assist in, or fully automate, code generation, testing, and optimization processes. System Engineering: Write clean, scalable, and maintainable code in both Java and Python to support AI backend infrastructure. Google Ecosystem Integration: Utilize Google ADK (AI Developer Kits) and related Google Cloud AI services (e.g., Vertex AI, Gemini APIs) to deploy robust AI solutions. Cross-Functional Collaboration: Work closely with product managers, data scientists, and frontend engineers to translate business requirements into technical AI solutions. Must-Have Qualifications: Programming Languages: Strong proficiency in both Java and Python, with a proven track record of building production-grade software. Google AI Tools: Hands-on experience with Google ADK (or equivalent Google Cloud AI/Vertex AI tools). LLM Expertise: Deep comfort level and practical experience working with Large Language Models (prompt engineering, fine-tuning, RAG architectures). Agentic Workflows: Demonstrable experience in building and orchestrating AI Agents (using frameworks like LangChain, LangGraph, or custom implementations). MCP Knowledge: Familiarity and practical experience with the Model Context Protocol (MCP) for standardizing AI interactions with external tools. Code Generation: Experience in leveraging AI tools or building pipelines specifically for code generation and software automation. Good-to-Have (Optional but highly valued): Experience with modern robust backend frameworks (e.g., Spring Boot for Java, FastAPI for Python). Familiarity with containerization and orchestration (Docker, Kubernetes). Experience with vector databases (e.g., Pinecone, Weaviate, Milvus).
