Pay Range: $83.44hr - $88.07hr Job Overview: We are hiring an AI Engineer to design, build, and operate agentic AI systems - LLM-powered agents that reason over enterprise data, call tools, and execute multi-step workflows end to end. This is a hands-on engineering role at the center of how the organization automates complex technical and operational work. The candidate will take ambiguous problems, decompose them into agent workflows, and ship production services that engineering teams depend on daily. This is an ideal role for an engineer with a strong Python and ML foundation who has already shipped LLM or RAG applications and now wants to go deeper into orchestration, tool-calling, and autonomous multi-agent architectures. The successful candidate will work within an established platform and architecture, with senior engineers available for design review - but will own components from prototype through production. Key Responsibilities: Agent Design & Orchestration: Build and ship LLM-powered agents that plan, call tools, and complete multi-step tasks using frameworks such as LangChain, LangGraph, or equivalent orchestration libraries. Implement multi-agent workflows - planner, executor, reviewer, and specialist roles - including state handling, hand-off logic, retries, and graceful failure paths. Design and expose tools to agents via function-calling and Model Context Protocol (MCP) servers, wrapping internal APIs, databases, and enterprise systems as safe, well-scoped agent capabilities. Retrieval & Knowledge Systems: Build and operationalize Retrieval-Augmented Generation (RAG) pipelines: chunking, embedding, vector and hybrid search, re-ranking, and context assembly. Integrate structured, semi-structured, and unstructured sources - SharePoint, Confluence, Jira, REST APIs, Excel, and SQL warehouses - into retrieval and agent context layers. Continuously improve answer grounding and reduce hallucination through better retrieval, prompt design, and context management. Engineering & Deployment: Develop production Python services and APIs for agent and model inference using FastAPI, containerized with Docker and deployed through standard CI/CD pipelines. Instrument agents with tracing, token and cost accounting, latency monitoring, and structured logging so that behavior is observable and debuggable in production. Apply MLOps and LLMOps practices including version control for prompts and configs, automated regression testing, and safe rollout of model or prompt changes. Evaluation & Quality: Build evaluation harnesses for agent workflows - golden datasets, task-level success metrics, LLM-as-judge scoring, and regression suites that gate releases. Measure and tune for accuracy, latency, and cost, and make explicit trade-offs between them based on the use case. Collaboration: Partner with domain experts, data engineers, and product stakeholders to turn manual workflows into reliable automated agents. Deliver results through interfaces users actually adopt - Streamlit apps, chat interfaces, dashboards, and embedded integrations. Write clear design notes and runbooks, and participate actively in code and design reviews. Required Qualifications: 3 5 years of professional software, ML, or data engineering experience, including at least 1 year building LLM-based or GenAI applications. Strong Python engineering skills - not just notebooks. Production code, testing, packaging, async patterns, and API development with FastAPI or similar. Hands-on experience with at least one LLM orchestration framework (LangChain, LangGraph, LlamaIndex, Semantic Kernel, or an equivalent) in a shipped application. Demonstrated experience building RAG systems: embeddings, vector stores (e.g., FAISS, pgvector, Pinecone, Chroma), and retrieval quality tuning. Practical command of prompt engineering, structured output, function/tool calling, and context-window management. Working knowledge of SQL and cloud data platforms (Snowflake, Databricks, or equivalent), and comfort writing queries against enterprise schemas. Experience containerizing and deploying services with Docker and shipping through CI/CD on a major cloud (Azure, AWS, or GCP). Solid grounding in ML fundamentals and familiarity with PyTorch, TensorFlow, or HuggingFace Transformers. Strong verbal and written communication skills, and the judgment to escalate when a problem is under-specified rather than guessing. Preferred Qualifications: Experience building or consuming Model Context Protocol (MCP) servers, or comparable agent tool-integration layers. Production experience with multi-agent or agentic workflow systems, including human-in-the-loop checkpoints and approval gates. Familiarity with LLM observability and evaluation tooling such as LangSmith, Langfuse, Weights & Biases, or MLflow. Experience with workflow orchestration tools - Airflow, Dagster, Prefect - or analytics engineering with DBT. Exposure to model fine-tuning or parameter-efficient adaptation (LoRA/PEFT) and knowing when it is - and is not - the right answer. Background in a technical engineering domain (semiconductor, hardware validation, manufacturing, or similar) where agents operate over specialized data. Experience with AI governance, responsible-AI review, or enterprise security requirements for LLM deployments. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
10/28/2026
Full time
Pay Range: $83.44hr - $88.07hr Job Overview: We are hiring an AI Engineer to design, build, and operate agentic AI systems - LLM-powered agents that reason over enterprise data, call tools, and execute multi-step workflows end to end. This is a hands-on engineering role at the center of how the organization automates complex technical and operational work. The candidate will take ambiguous problems, decompose them into agent workflows, and ship production services that engineering teams depend on daily. This is an ideal role for an engineer with a strong Python and ML foundation who has already shipped LLM or RAG applications and now wants to go deeper into orchestration, tool-calling, and autonomous multi-agent architectures. The successful candidate will work within an established platform and architecture, with senior engineers available for design review - but will own components from prototype through production. Key Responsibilities: Agent Design & Orchestration: Build and ship LLM-powered agents that plan, call tools, and complete multi-step tasks using frameworks such as LangChain, LangGraph, or equivalent orchestration libraries. Implement multi-agent workflows - planner, executor, reviewer, and specialist roles - including state handling, hand-off logic, retries, and graceful failure paths. Design and expose tools to agents via function-calling and Model Context Protocol (MCP) servers, wrapping internal APIs, databases, and enterprise systems as safe, well-scoped agent capabilities. Retrieval & Knowledge Systems: Build and operationalize Retrieval-Augmented Generation (RAG) pipelines: chunking, embedding, vector and hybrid search, re-ranking, and context assembly. Integrate structured, semi-structured, and unstructured sources - SharePoint, Confluence, Jira, REST APIs, Excel, and SQL warehouses - into retrieval and agent context layers. Continuously improve answer grounding and reduce hallucination through better retrieval, prompt design, and context management. Engineering & Deployment: Develop production Python services and APIs for agent and model inference using FastAPI, containerized with Docker and deployed through standard CI/CD pipelines. Instrument agents with tracing, token and cost accounting, latency monitoring, and structured logging so that behavior is observable and debuggable in production. Apply MLOps and LLMOps practices including version control for prompts and configs, automated regression testing, and safe rollout of model or prompt changes. Evaluation & Quality: Build evaluation harnesses for agent workflows - golden datasets, task-level success metrics, LLM-as-judge scoring, and regression suites that gate releases. Measure and tune for accuracy, latency, and cost, and make explicit trade-offs between them based on the use case. Collaboration: Partner with domain experts, data engineers, and product stakeholders to turn manual workflows into reliable automated agents. Deliver results through interfaces users actually adopt - Streamlit apps, chat interfaces, dashboards, and embedded integrations. Write clear design notes and runbooks, and participate actively in code and design reviews. Required Qualifications: 3 5 years of professional software, ML, or data engineering experience, including at least 1 year building LLM-based or GenAI applications. Strong Python engineering skills - not just notebooks. Production code, testing, packaging, async patterns, and API development with FastAPI or similar. Hands-on experience with at least one LLM orchestration framework (LangChain, LangGraph, LlamaIndex, Semantic Kernel, or an equivalent) in a shipped application. Demonstrated experience building RAG systems: embeddings, vector stores (e.g., FAISS, pgvector, Pinecone, Chroma), and retrieval quality tuning. Practical command of prompt engineering, structured output, function/tool calling, and context-window management. Working knowledge of SQL and cloud data platforms (Snowflake, Databricks, or equivalent), and comfort writing queries against enterprise schemas. Experience containerizing and deploying services with Docker and shipping through CI/CD on a major cloud (Azure, AWS, or GCP). Solid grounding in ML fundamentals and familiarity with PyTorch, TensorFlow, or HuggingFace Transformers. Strong verbal and written communication skills, and the judgment to escalate when a problem is under-specified rather than guessing. Preferred Qualifications: Experience building or consuming Model Context Protocol (MCP) servers, or comparable agent tool-integration layers. Production experience with multi-agent or agentic workflow systems, including human-in-the-loop checkpoints and approval gates. Familiarity with LLM observability and evaluation tooling such as LangSmith, Langfuse, Weights & Biases, or MLflow. Experience with workflow orchestration tools - Airflow, Dagster, Prefect - or analytics engineering with DBT. Exposure to model fine-tuning or parameter-efficient adaptation (LoRA/PEFT) and knowing when it is - and is not - the right answer. Background in a technical engineering domain (semiconductor, hardware validation, manufacturing, or similar) where agents operate over specialized data. Experience with AI governance, responsible-AI review, or enterprise security requirements for LLM deployments. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. The Principal AI Engineer provides the highest level of individual-contributor technical leadership for the company's AI platform, operating one level above the Lead AI Engineer role. This person sets the multi-year architecture strategy for agentic and LLM-powered systems - spanning model strategy, retrieval-augmented generation (RAG), tool/connector ecosystems, and evaluation/governance tooling - across every team that builds on the AI platform, not just one. In a highly regulated healthcare environment, the Principal AI Engineer resolves the hardest, most ambiguous technical problems facing the platform, is a primary technical voice with executive leadership on AI strategy and risk, and shapes how the entire engineering organization adopts AI safely and effectively. Success is measured by the durability and adoption of the technical strategy this role sets across multiple teams, this role's ability to unblock cross-organizational technical risk before it becomes a delivery or compliance problem, and its influence on AI investment decisions at the executive level. Key Responsibilities Enterprise AI Strategy: Own the multi-year technical strategy and roadmap for the AI platform across all teams building on it, not a single product or team - model strategy, RAG/retrieval architecture, connector ecosystems, and evaluation frameworks. Technical Authority: Serve as the final technical escalation point for the hardest, most ambiguous architecture and reliability problems on the AI platform, spanning multiple teams and systems. Cross-Team Standards: Set architecture, security, and evaluation standards that multiple Lead AI Engineers and their teams adopt; audit and evolve those standards as the platform and threat landscape change. Executive Advisory: Serve as a primary technical voice to VP- and executive-level leadership on AI platform strategy, model risk, and investment trade-offs. Deep Technical Delivery: Personally lead architecture and prototyping for the organization's highest-ambiguity, highest-risk AI initiatives - the problems no existing team or pattern already solves. Mentorship at Scale: Develop Lead and Senior AI Engineers into stronger technical leaders through design review, technical coaching, and structured knowledge-sharing across teams. Responsible AI & Governance: Shape enterprise-wide AI governance policy - model risk review, connector/tool approval frameworks, and PHI-handling standards - in partnership with Legal/Compliance/Privacy leadership. Vendor & Model Strategy at Scale: Own evaluation and long-term strategy for foundation model providers and AI infrastructure vendors across the company, not just a single team's stack. External Technical Representation: Represent the company's AI engineering capability externally - industry forums, partner/vendor technical relationships, and recruiting for senior technical talent. Organizational Impact: Influence AI platform direction and investment across the broader enterprise, extending beyond a single business unit. Priority will be given to candidates who reside in the Columbus, OH metropolitan area. We are unable to provide sponsorship for this role, presently or in the future. Minimum Requirements Bachelor's degree or equivalent experience; typically 12+ years of software engineering experience, including 6+ years focused on AI/ML, LLM, or NLP systems in production and 3+ years operating as a senior individual-contributor technical leader. Fewer years required with a relevant Master's or Doctorate. Critical Skills One level of demonstrated technical leadership above a Lead AI Engineer - e.g., setting technical direction and standards across multiple teams, not just owning architecture for a single team or platform area. Proven track record setting technical strategy that multiple teams adopt, not just delivering within one team's roadmap. Deep, hands-on expertise building production LLM applications at scale: prompt engineering, RAG, agentic workflows, tool use, and evaluation harnesses - with the judgment to know which patterns will and won't hold up over multiple years. Experience with commercial large language model APIs (e.g., Anthropic, OpenAI) and tool-integration protocols such as Model Context Protocol (MCP), including evaluating emerging alternatives. Strong software engineering fundamentals: Python and/or Java, REST APIs, event-driven architecture, microservices, and cloud-native design (Azure preferred; AWS/GCP acceptable). Experience with vector/graph retrieval systems, embeddings, and knowledge-base architectures at production scale across multiple products. Deep working knowledge of LLMOps/MLOps: usage attribution, cost/spend monitoring, abuse detection, and model evaluation pipelines, including designing these systems from scratch. Demonstrated ability to influence and align senior engineering and executive stakeholders without direct authority. Deep familiarity with regulated/healthcare data environments and AI governance frameworks (model risk review, connector approval tiers, PHI handling). Preferred Qualifications Advanced degree (M.S. or Ph.D.) in Computer Science, AI/ML, or a related technical field. Prior experience as a Principal, Staff, or Distinguished Engineer setting AI/ML technical strategy across multiple teams inside healthcare IT or another highly regulated industry. Track record of publications, patents, open-source contributions, or industry speaking on applied AI/LLM systems. Experience shaping enterprise-wide AI governance policy in partnership with Legal, Compliance, and Privacy leadership. Additional Skills Deep awareness of the fast-moving GenAI/agentic landscape and a demonstrated ability to separate durable technical bets from hype. Experience managing strategic relationships with AI infrastructure vendors and model providers at the enterprise level. Exceptional written and verbal communication for executive, board-level, and external technical audiences. We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $195,000 - $325,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!
10/27/2026
Full time
McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. The Principal AI Engineer provides the highest level of individual-contributor technical leadership for the company's AI platform, operating one level above the Lead AI Engineer role. This person sets the multi-year architecture strategy for agentic and LLM-powered systems - spanning model strategy, retrieval-augmented generation (RAG), tool/connector ecosystems, and evaluation/governance tooling - across every team that builds on the AI platform, not just one. In a highly regulated healthcare environment, the Principal AI Engineer resolves the hardest, most ambiguous technical problems facing the platform, is a primary technical voice with executive leadership on AI strategy and risk, and shapes how the entire engineering organization adopts AI safely and effectively. Success is measured by the durability and adoption of the technical strategy this role sets across multiple teams, this role's ability to unblock cross-organizational technical risk before it becomes a delivery or compliance problem, and its influence on AI investment decisions at the executive level. Key Responsibilities Enterprise AI Strategy: Own the multi-year technical strategy and roadmap for the AI platform across all teams building on it, not a single product or team - model strategy, RAG/retrieval architecture, connector ecosystems, and evaluation frameworks. Technical Authority: Serve as the final technical escalation point for the hardest, most ambiguous architecture and reliability problems on the AI platform, spanning multiple teams and systems. Cross-Team Standards: Set architecture, security, and evaluation standards that multiple Lead AI Engineers and their teams adopt; audit and evolve those standards as the platform and threat landscape change. Executive Advisory: Serve as a primary technical voice to VP- and executive-level leadership on AI platform strategy, model risk, and investment trade-offs. Deep Technical Delivery: Personally lead architecture and prototyping for the organization's highest-ambiguity, highest-risk AI initiatives - the problems no existing team or pattern already solves. Mentorship at Scale: Develop Lead and Senior AI Engineers into stronger technical leaders through design review, technical coaching, and structured knowledge-sharing across teams. Responsible AI & Governance: Shape enterprise-wide AI governance policy - model risk review, connector/tool approval frameworks, and PHI-handling standards - in partnership with Legal/Compliance/Privacy leadership. Vendor & Model Strategy at Scale: Own evaluation and long-term strategy for foundation model providers and AI infrastructure vendors across the company, not just a single team's stack. External Technical Representation: Represent the company's AI engineering capability externally - industry forums, partner/vendor technical relationships, and recruiting for senior technical talent. Organizational Impact: Influence AI platform direction and investment across the broader enterprise, extending beyond a single business unit. Priority will be given to candidates who reside in the Columbus, OH metropolitan area. We are unable to provide sponsorship for this role, presently or in the future. Minimum Requirements Bachelor's degree or equivalent experience; typically 12+ years of software engineering experience, including 6+ years focused on AI/ML, LLM, or NLP systems in production and 3+ years operating as a senior individual-contributor technical leader. Fewer years required with a relevant Master's or Doctorate. Critical Skills One level of demonstrated technical leadership above a Lead AI Engineer - e.g., setting technical direction and standards across multiple teams, not just owning architecture for a single team or platform area. Proven track record setting technical strategy that multiple teams adopt, not just delivering within one team's roadmap. Deep, hands-on expertise building production LLM applications at scale: prompt engineering, RAG, agentic workflows, tool use, and evaluation harnesses - with the judgment to know which patterns will and won't hold up over multiple years. Experience with commercial large language model APIs (e.g., Anthropic, OpenAI) and tool-integration protocols such as Model Context Protocol (MCP), including evaluating emerging alternatives. Strong software engineering fundamentals: Python and/or Java, REST APIs, event-driven architecture, microservices, and cloud-native design (Azure preferred; AWS/GCP acceptable). Experience with vector/graph retrieval systems, embeddings, and knowledge-base architectures at production scale across multiple products. Deep working knowledge of LLMOps/MLOps: usage attribution, cost/spend monitoring, abuse detection, and model evaluation pipelines, including designing these systems from scratch. Demonstrated ability to influence and align senior engineering and executive stakeholders without direct authority. Deep familiarity with regulated/healthcare data environments and AI governance frameworks (model risk review, connector approval tiers, PHI handling). Preferred Qualifications Advanced degree (M.S. or Ph.D.) in Computer Science, AI/ML, or a related technical field. Prior experience as a Principal, Staff, or Distinguished Engineer setting AI/ML technical strategy across multiple teams inside healthcare IT or another highly regulated industry. Track record of publications, patents, open-source contributions, or industry speaking on applied AI/LLM systems. Experience shaping enterprise-wide AI governance policy in partnership with Legal, Compliance, and Privacy leadership. Additional Skills Deep awareness of the fast-moving GenAI/agentic landscape and a demonstrated ability to separate durable technical bets from hype. Experience managing strategic relationships with AI infrastructure vendors and model providers at the enterprise level. Exceptional written and verbal communication for executive, board-level, and external technical audiences. We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $195,000 - $325,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!
McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. About the Team Our AI Enablement team drives enterprise-wide adoption of generative AI, spanning platform engineering, governance, and hands-on delivery of AI-powered products and internal tooling. The team builds and operates the infrastructure, integrations, and knowledge systems that let engineering and business teams safely adopt large language models at scale. The Lead AI Engineer joins as the senior technical authority on this team, setting the architectural direction that Senior and Associate AI Engineers build against. Position Summary The Lead AI Engineer provides technical leadership and architecture ownership for the company's AI platform and its embedded delivery initiatives, operating one level above the Senior AI Engineer role. This person owns the technical design of agentic and LLM-powered systems built on large language model APIs, including retrieval-augmented generation (RAG), tool/connector integration, and evaluation/governance tooling, in a highly regulated healthcare environment. The Lead AI Engineer sets architectural standards across AI-embedded product delivery, mentors Senior and Associate AI Engineers, and represents AI platform architecture to engineering leadership and Legal/Compliance/Privacy. Success is measured by the reliability, security, and scalability of the AI systems shipped, the rate at which Senior/Associate engineers adopt the architectural standards this role sets, and this role's influence on AI platform decisions across the organization. Key Responsibilities AI Architecture Leadership: Define the technical architecture and roadmap for LLM/agentic systems - model routing across cloud AI platforms, connector/integration patterns, RAG pipelines, and prompt/evaluation frameworks. Solution Design & Governance: Lead architecture reviews for new AI capabilities and ensure designs meet enterprise security, privacy, and data-provenance requirements. Platform Ownership: Drive the technical design of shared AI platform capabilities, including knowledge-retrieval systems, citation/traceability of AI-generated outputs, and permission-aware access to data. Hands-On Delivery: Personally architect and build high-complexity components on priority AI delivery initiatives where a working system, not just a design doc, is the deliverable. Technical Mentorship: Set coding, architecture, and evaluation standards for Senior and Associate AI Engineers; run design reviews and pair on complex agentic workflows. LLMOps & Responsible AI: Establish secure, observable, and cost-attributed AI operations - usage/spend attribution, abuse monitoring, model evaluation, and rollback strategy - for production LLM systems. Cross-Functional Architecture: Partner with engineering managers, product, platform, and Legal/Compliance to align AI architecture decisions with delivery timelines and enterprise standards. Vendor & Model Strategy: Evaluate foundation model providers and AI tooling vendors; make build-vs-buy and model-selection recommendations. Compliance & Risk: Ensure AI systems handling protected health information (PHI) meet regulatory and enterprise governance requirements. Organizational Impact: Represent AI architecture in cross-company forums and influence enterprise-wide AI platform direction. Priority will be given to candidates who reside in the Columbus, OH metropolitan area. We are unable to provide sponsorship for this role, presently or in the future. Minimum Requirements Bachelor's degree or equivalent experience; typically 8+ years of software engineering experience, including 3+ years focused on AI/ML, LLM, or NLP systems in production. Fewer years required with a relevant Master's or Doctorate. Critical Skills One level of demonstrated technical leadership above a Senior AI Engineer - e.g., owning architecture for a team or platform area, not just a single feature. Deep, hands-on expertise building production LLM applications: prompt engineering, RAG/GraphRAG, agentic workflows, tool use, and evaluation harnesses. Experience with commercial large language model APIs (e.g., Anthropic, OpenAI) and tool-integration protocols such as Model Context Protocol (MCP). Strong software engineering fundamentals: Python and/or Java, REST APIs, event-driven architecture, microservices, and cloud-native design (Azure preferred; AWS/GCP acceptable). Experience with vector/graph retrieval systems, embeddings, and knowledge-base architectures at production scale. Working knowledge of LLMOps/MLOps: usage attribution, cost/spend monitoring, abuse detection, and model evaluation pipelines. Ability to set architectural standards and mentor other engineers through design review and pairing, not just documentation. Familiarity with regulated/healthcare data environments and AI governance frameworks (model risk review, connector approval tiers, PHI handling) is a plus. Preferred Qualifications Advanced degree (M.S. or higher) in Computer Science, AI/ML, or a related technical field. Prior experience leading AI/ML platform or applied-AI teams inside healthcare IT or another highly regulated industry. Experience modernizing legacy systems toward AI-embedded workflows (agent-assisted development, AI-powered risk scoring, AI-driven automation). Experience with Confluence/Jira-based engineering governance and enterprise architecture review boards. Additional Skills Strong awareness of the fast-moving GenAI/agentic landscape and a demonstrated habit of continuous learning. Experience managing relationships with AI tooling vendors and model providers. Clear written and verbal communication for both technical and executive audiences We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $155,300 - $258,800 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!
10/27/2026
Full time
McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. About the Team Our AI Enablement team drives enterprise-wide adoption of generative AI, spanning platform engineering, governance, and hands-on delivery of AI-powered products and internal tooling. The team builds and operates the infrastructure, integrations, and knowledge systems that let engineering and business teams safely adopt large language models at scale. The Lead AI Engineer joins as the senior technical authority on this team, setting the architectural direction that Senior and Associate AI Engineers build against. Position Summary The Lead AI Engineer provides technical leadership and architecture ownership for the company's AI platform and its embedded delivery initiatives, operating one level above the Senior AI Engineer role. This person owns the technical design of agentic and LLM-powered systems built on large language model APIs, including retrieval-augmented generation (RAG), tool/connector integration, and evaluation/governance tooling, in a highly regulated healthcare environment. The Lead AI Engineer sets architectural standards across AI-embedded product delivery, mentors Senior and Associate AI Engineers, and represents AI platform architecture to engineering leadership and Legal/Compliance/Privacy. Success is measured by the reliability, security, and scalability of the AI systems shipped, the rate at which Senior/Associate engineers adopt the architectural standards this role sets, and this role's influence on AI platform decisions across the organization. Key Responsibilities AI Architecture Leadership: Define the technical architecture and roadmap for LLM/agentic systems - model routing across cloud AI platforms, connector/integration patterns, RAG pipelines, and prompt/evaluation frameworks. Solution Design & Governance: Lead architecture reviews for new AI capabilities and ensure designs meet enterprise security, privacy, and data-provenance requirements. Platform Ownership: Drive the technical design of shared AI platform capabilities, including knowledge-retrieval systems, citation/traceability of AI-generated outputs, and permission-aware access to data. Hands-On Delivery: Personally architect and build high-complexity components on priority AI delivery initiatives where a working system, not just a design doc, is the deliverable. Technical Mentorship: Set coding, architecture, and evaluation standards for Senior and Associate AI Engineers; run design reviews and pair on complex agentic workflows. LLMOps & Responsible AI: Establish secure, observable, and cost-attributed AI operations - usage/spend attribution, abuse monitoring, model evaluation, and rollback strategy - for production LLM systems. Cross-Functional Architecture: Partner with engineering managers, product, platform, and Legal/Compliance to align AI architecture decisions with delivery timelines and enterprise standards. Vendor & Model Strategy: Evaluate foundation model providers and AI tooling vendors; make build-vs-buy and model-selection recommendations. Compliance & Risk: Ensure AI systems handling protected health information (PHI) meet regulatory and enterprise governance requirements. Organizational Impact: Represent AI architecture in cross-company forums and influence enterprise-wide AI platform direction. Priority will be given to candidates who reside in the Columbus, OH metropolitan area. We are unable to provide sponsorship for this role, presently or in the future. Minimum Requirements Bachelor's degree or equivalent experience; typically 8+ years of software engineering experience, including 3+ years focused on AI/ML, LLM, or NLP systems in production. Fewer years required with a relevant Master's or Doctorate. Critical Skills One level of demonstrated technical leadership above a Senior AI Engineer - e.g., owning architecture for a team or platform area, not just a single feature. Deep, hands-on expertise building production LLM applications: prompt engineering, RAG/GraphRAG, agentic workflows, tool use, and evaluation harnesses. Experience with commercial large language model APIs (e.g., Anthropic, OpenAI) and tool-integration protocols such as Model Context Protocol (MCP). Strong software engineering fundamentals: Python and/or Java, REST APIs, event-driven architecture, microservices, and cloud-native design (Azure preferred; AWS/GCP acceptable). Experience with vector/graph retrieval systems, embeddings, and knowledge-base architectures at production scale. Working knowledge of LLMOps/MLOps: usage attribution, cost/spend monitoring, abuse detection, and model evaluation pipelines. Ability to set architectural standards and mentor other engineers through design review and pairing, not just documentation. Familiarity with regulated/healthcare data environments and AI governance frameworks (model risk review, connector approval tiers, PHI handling) is a plus. Preferred Qualifications Advanced degree (M.S. or higher) in Computer Science, AI/ML, or a related technical field. Prior experience leading AI/ML platform or applied-AI teams inside healthcare IT or another highly regulated industry. Experience modernizing legacy systems toward AI-embedded workflows (agent-assisted development, AI-powered risk scoring, AI-driven automation). Experience with Confluence/Jira-based engineering governance and enterprise architecture review boards. Additional Skills Strong awareness of the fast-moving GenAI/agentic landscape and a demonstrated habit of continuous learning. Experience managing relationships with AI tooling vendors and model providers. Clear written and verbal communication for both technical and executive audiences We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $155,300 - $258,800 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!
Credit Acceptance Corporation
San Francisco, California
At Credit Acceptance, we are building on the foundation of a successful business while investing in the future through technology, data, AI, and continuous improvement. As we continue to evolve, our Team Members play an active role in improving experiences, driving meaningful change, and shaping the future of our business. We foster an environment where people are empowered to contribute ideas, develop their careers, make an impact, and help lead our continued transformation. Our Engineering teams are helping shape the future of Credit Acceptance through innovation. Team Members collaborate across the organization to solve complex challenges, build impactful solutions, challenge the status quo, and support informed business decisions. We value curiosity, continuous learning, and new ideas from people who are passionate about driving modernization and meaningful business impact. Outcomes and Activities: Own the deployment path for ML and GenAI models end to end training and inference pipelines, model registry and versioning, serving endpoints, and controlled promotion across development, QA and production. Own runtime health for models in production: monitoring, alerting, drift and quality-regression detection, latency and throughput objectives, capacity and autoscaling behavior, and incident response through to root cause and a closed corrective action. Operate the agent runtime layer. Route production agents through the enterprise AI Gateway and MCP Gateway rather than direct model and tool access, migrate existing agents onto that governed path, and keep tool surfaces scoped, versioned and least-privilege as they change. Own evaluation of agents in production, not just before release. Online scoring and behavioral monitoring, quality-regression and drift detection against pinned baselines, sampling and judge pipelines, and the release-gate mechanics that stop a regression from shipping. Build and maintain the observability and evaluation substrate other teams depend on: trace and telemetry capture including multi-turn and multi-step agent traces, logging standards, evaluation pipeline plumbing, and the data contracts underneath them. Own platform unit economics. Measure and manage cost per inference, per document and per interaction, and produce the platform and infrastructure cost analysis that informs build-versus-buy and hosting decisions. Make the paved road real. Deliver reusable pipeline templates, deployment patterns, reference implementations and internal tooling so product teams adopt the standard path because it is faster, not because it is mandated. Partner with Cloud Engineering, Data Engineering, Security and SRE so the ML platform sits inside enterprise governance, identity and observability rather than beside it. Respond to AI-specific production incidents and drive them to a closed corrective action: prompt injection attempts, rogue-agent cost spikes, data-classification exposure through a tool call, delegation abuse between agents, and model endpoint failures. Maintain the architecture documentation and system diagrams for the ML platform and keep them accurate enough to be used in design review. Mentor engineers and interns on production ML practice and raise the operating standard through design and code review rather than through rework. Competencies: The following items detail how you will be successful in this role. Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer's shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience. Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions. One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively. Owner's Mindset: Owner's Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business. Requirements Bachelor's degree in Computer Science, Engineering, Statistics or a relevant technical field with at least 7 years of relevant experience, or a Master's degree in one of those fields with at least 5 years of relevant experience. 5+ years building and operating production ML or AI systems, with direct ownership of at least two of: training or inference pipelines, model serving infrastructure, model registry and versioning, or production monitoring and alerting. Demonstrated ownership of a production ML or AI service through its full operating life: deployed it, monitored it, was paged for it, diagnosed a failure, and shipped the fix. Strong Python and SQL, with production-quality engineering practice: version control, testing, code review, and CI/CD applied to ML workloads rather than only to application code. Hands-on experience with a cloud ML platform in production. AWS and Databricks strongly preferred, including model serving, job orchestration, and a model registry or experiment tracking system such as MLflow. Working knowledge of how LLM and GenAI workloads differ operationally from traditional ML: token cost and latency behavior, caching and batching, non-deterministic output. Experience running LLM or agent applications behind a gateway or proxy layer, and able to build best practices for model routing and fallback, credential and key management, rate limiting and budget enforcement. Working knowledge of tool-calling architecture for agents, including the Model Context Protocol: what an MCP server is, how tools are scoped and authorized, and why tool access is brokered through a gateway rather than granted directly. Experience with containerization and infrastructure as code. Ability to communicate technical and non-technical trade-offs clearly in writing to an audience that includes both engineers and non-engineers. Candidates must reside in or be willing to relocate to one of the following hub locations: the greater Southfield, Michigan, area; the greater Bellevue, Washington, area; or the San Francisco Bay Area, California. Preferred Production experience with GPU-backed model serving, including autoscaling behavior under concurrent load, cold-start management and cost control. Experience with OpenTelemetry and an enterprise observability platform such as Dynatrace, including instrumenting AI workloads rather than only conventional services. Experience with model serving efficiency techniques: quantization, parameter-efficient fine-tuning, distillation or inference optimization. Experience with data governance and access control on a lakehouse platform, such as Databricks Unity Catalog. Experience operating AI systems in a regulated industry, particularly financial services, including auditability, retention and access-control requirements. Experience with agentic or multi-step AI systems in production, including tracing and debugging multi-turn behavior. Hands-on experience with a managed agent or tool gateway, such as AWS Bedrock AgentCore Gateway, and with running MCP servers in a governed environment. Familiarity with emerging agent interoperability and agent identity standards, including Agent2Agent (A2A) style agent-to-agent delegation, agent discovery and capability advertisement, and workload identity for non-human actors. Knowledge and Skills Ability to communicate complex technical information, both verbal and written, to all levels, including senior leadership. Ability to solve problems at the source by offering simple, working solutions. Responds promptly and effectively to resolve incidents, tasks, and projects. Demonstrated ability and motivation to teach others. Ability to gain the trust of others and build solid relationships across and vertically throughout the organization. Effectively prioritize and execute tasks in a high-pressure environment. Target Compensation: This position offers a competitive base salary, with a range of $184,943- $258,920. This position is eligible for an annual variable incentive of cash and equity, between 10- 20% of base salary. Actual amounts are based on individual performance. Final compensation within the range is influenced by many factors including assigned hub location, role-specific skills, depth and experience level, industry background, relevant education and certifications. This position follows a hybrid work model, offering two work-from-home days and three standard in-office days each week with the team and key stakeholders at the local office. INDENGLP Benefits Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/ dental/vision and many nonstandard benefits that make us a Great Place to Work Our Company Values: To be successful in this role, Team Members need to be: Positive by maintaining resiliency and focusing on solutions Respectful by collaborating and actively listening Insightful by cultivating innovation, accumulating business and role specific knowledge, demonstrating self-awareness and making quality decisions Direct by effectively communicating and conveying courage Earnest by taking accountability . click apply for full job details
10/27/2026
Full time
At Credit Acceptance, we are building on the foundation of a successful business while investing in the future through technology, data, AI, and continuous improvement. As we continue to evolve, our Team Members play an active role in improving experiences, driving meaningful change, and shaping the future of our business. We foster an environment where people are empowered to contribute ideas, develop their careers, make an impact, and help lead our continued transformation. Our Engineering teams are helping shape the future of Credit Acceptance through innovation. Team Members collaborate across the organization to solve complex challenges, build impactful solutions, challenge the status quo, and support informed business decisions. We value curiosity, continuous learning, and new ideas from people who are passionate about driving modernization and meaningful business impact. Outcomes and Activities: Own the deployment path for ML and GenAI models end to end training and inference pipelines, model registry and versioning, serving endpoints, and controlled promotion across development, QA and production. Own runtime health for models in production: monitoring, alerting, drift and quality-regression detection, latency and throughput objectives, capacity and autoscaling behavior, and incident response through to root cause and a closed corrective action. Operate the agent runtime layer. Route production agents through the enterprise AI Gateway and MCP Gateway rather than direct model and tool access, migrate existing agents onto that governed path, and keep tool surfaces scoped, versioned and least-privilege as they change. Own evaluation of agents in production, not just before release. Online scoring and behavioral monitoring, quality-regression and drift detection against pinned baselines, sampling and judge pipelines, and the release-gate mechanics that stop a regression from shipping. Build and maintain the observability and evaluation substrate other teams depend on: trace and telemetry capture including multi-turn and multi-step agent traces, logging standards, evaluation pipeline plumbing, and the data contracts underneath them. Own platform unit economics. Measure and manage cost per inference, per document and per interaction, and produce the platform and infrastructure cost analysis that informs build-versus-buy and hosting decisions. Make the paved road real. Deliver reusable pipeline templates, deployment patterns, reference implementations and internal tooling so product teams adopt the standard path because it is faster, not because it is mandated. Partner with Cloud Engineering, Data Engineering, Security and SRE so the ML platform sits inside enterprise governance, identity and observability rather than beside it. Respond to AI-specific production incidents and drive them to a closed corrective action: prompt injection attempts, rogue-agent cost spikes, data-classification exposure through a tool call, delegation abuse between agents, and model endpoint failures. Maintain the architecture documentation and system diagrams for the ML platform and keep them accurate enough to be used in design review. Mentor engineers and interns on production ML practice and raise the operating standard through design and code review rather than through rework. Competencies: The following items detail how you will be successful in this role. Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer's shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience. Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions. One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively. Owner's Mindset: Owner's Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business. Requirements Bachelor's degree in Computer Science, Engineering, Statistics or a relevant technical field with at least 7 years of relevant experience, or a Master's degree in one of those fields with at least 5 years of relevant experience. 5+ years building and operating production ML or AI systems, with direct ownership of at least two of: training or inference pipelines, model serving infrastructure, model registry and versioning, or production monitoring and alerting. Demonstrated ownership of a production ML or AI service through its full operating life: deployed it, monitored it, was paged for it, diagnosed a failure, and shipped the fix. Strong Python and SQL, with production-quality engineering practice: version control, testing, code review, and CI/CD applied to ML workloads rather than only to application code. Hands-on experience with a cloud ML platform in production. AWS and Databricks strongly preferred, including model serving, job orchestration, and a model registry or experiment tracking system such as MLflow. Working knowledge of how LLM and GenAI workloads differ operationally from traditional ML: token cost and latency behavior, caching and batching, non-deterministic output. Experience running LLM or agent applications behind a gateway or proxy layer, and able to build best practices for model routing and fallback, credential and key management, rate limiting and budget enforcement. Working knowledge of tool-calling architecture for agents, including the Model Context Protocol: what an MCP server is, how tools are scoped and authorized, and why tool access is brokered through a gateway rather than granted directly. Experience with containerization and infrastructure as code. Ability to communicate technical and non-technical trade-offs clearly in writing to an audience that includes both engineers and non-engineers. Candidates must reside in or be willing to relocate to one of the following hub locations: the greater Southfield, Michigan, area; the greater Bellevue, Washington, area; or the San Francisco Bay Area, California. Preferred Production experience with GPU-backed model serving, including autoscaling behavior under concurrent load, cold-start management and cost control. Experience with OpenTelemetry and an enterprise observability platform such as Dynatrace, including instrumenting AI workloads rather than only conventional services. Experience with model serving efficiency techniques: quantization, parameter-efficient fine-tuning, distillation or inference optimization. Experience with data governance and access control on a lakehouse platform, such as Databricks Unity Catalog. Experience operating AI systems in a regulated industry, particularly financial services, including auditability, retention and access-control requirements. Experience with agentic or multi-step AI systems in production, including tracing and debugging multi-turn behavior. Hands-on experience with a managed agent or tool gateway, such as AWS Bedrock AgentCore Gateway, and with running MCP servers in a governed environment. Familiarity with emerging agent interoperability and agent identity standards, including Agent2Agent (A2A) style agent-to-agent delegation, agent discovery and capability advertisement, and workload identity for non-human actors. Knowledge and Skills Ability to communicate complex technical information, both verbal and written, to all levels, including senior leadership. Ability to solve problems at the source by offering simple, working solutions. Responds promptly and effectively to resolve incidents, tasks, and projects. Demonstrated ability and motivation to teach others. Ability to gain the trust of others and build solid relationships across and vertically throughout the organization. Effectively prioritize and execute tasks in a high-pressure environment. Target Compensation: This position offers a competitive base salary, with a range of $184,943- $258,920. This position is eligible for an annual variable incentive of cash and equity, between 10- 20% of base salary. Actual amounts are based on individual performance. Final compensation within the range is influenced by many factors including assigned hub location, role-specific skills, depth and experience level, industry background, relevant education and certifications. This position follows a hybrid work model, offering two work-from-home days and three standard in-office days each week with the team and key stakeholders at the local office. INDENGLP Benefits Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/ dental/vision and many nonstandard benefits that make us a Great Place to Work Our Company Values: To be successful in this role, Team Members need to be: Positive by maintaining resiliency and focusing on solutions Respectful by collaborating and actively listening Insightful by cultivating innovation, accumulating business and role specific knowledge, demonstrating self-awareness and making quality decisions Direct by effectively communicating and conveying courage Earnest by taking accountability . click apply for full job details
Job Title: AI Architect (GenAI / MLOps) Position Overview Turning promising AI ideas into production systems that are secure, compliant, and built to scale is the hard part of enterprise AI. This role sits at the center of that work. You'll own the architecture for enterprise AI and GenAI solutions end to end, from data foundations and model selection through deployment, governance, and monitoring. You'll work closely with business, data, engineering, and risk teams, and you'll advise executives on what's feasible, what's worth it, and what the trade-offs are. What You'll Do / Key Responsibilities Design end-to-end AI/ML and GenAI architectures (RAG, agents, fine-tuning, model serving) that integrate with existing enterprise platforms Evaluate and select LLMs, vector databases, orchestration frameworks, and MLOps tooling Define reference architectures, patterns, and standards for reuse across teams Partner with data engineering on pipelines, feature stores, and data quality for AI workloads Build in responsible AI: model risk management, explainability, bias testing, audit trails, and regulatory compliance Establish MLOps/LLMOps practices covering CI/CD, evaluation, observability, drift detection, and cost control Lead proofs of concept and pilots, and move them to production Advise stakeholders and executives on AI strategy, feasibility, and trade-offs Mentor engineers and data scientists, and run architecture reviews Required Qualifications 10+ years in software or data engineering, with 4+ years architecting AI/ML solutions in production Hands-on experience with LLMs and GenAI: prompt engineering, RAG, embeddings, and agent frameworks (e.g., LangChain, LlamaIndex) Strong Python skills and fluency in ML frameworks (PyTorch, TensorFlow, scikit-learn) Deep experience with at least one major cloud (AWS, Azure, or GCP) and its AI services (e.g., SageMaker, Bedrock, Azure OpenAI, Vertex AI) Experience with MLOps tooling (MLflow, Kubeflow, Docker, Kubernetes, CI/CD) Solid grounding in data architecture: data lakes/warehouses, streaming, APIs, and microservices Knowledge of security, privacy, and governance for AI systems Excellent communication, with the ability to explain technical trade-offs to non-technical leaders Preferred Qualifications Experience in banking, insurance, or asset/wealth management, including regulatory expectations (e.g., SR 11-7 model risk) Experience with vector databases (Pinecone, pgvector, OpenSearch) and fine-tuning or evaluation frameworks Cloud or architecture certifications (e.g., AWS ML Specialty, Azure AI Engineer, TOGAF) Master's or PhD in Computer Science, Data Science, or a related field What Makes HTC A Great Place To Build Your Future HTC Global Services wants you to join our team. Come build new things with us and advance your career. At HTC Global, you'll collaborate with experts, work alongside clients, and be part of high-performing teams driving success together. You'll have long-term opportunities to grow your career and develop skills in the latest emerging technologies. At HTC Global Services, our employees have access to a comprehensive benefits package. Benefits can include Group Health (Medical, Dental, and Vision), Paid Time Off, Paid Holidays, 401(k) matching, Group Life and Disability insurance, Professional Development opportunities, Wellness programs, and a variety of other perks. Our success as a company is built on inclusion and diversity. HTC Global Services is committed to providing a workplace free from discrimination and harassment, where every employee is treated with dignity and respect. We celebrate differences and believe that diverse cultures, perspectives, and skills drive innovation and success. HTC is an Equal Opportunity Employer and a proud National Minority Supplier. We seek to empower each individual, fostering an environment where everyone feels valued, included, and respected.
10/27/2026
Full time
Job Title: AI Architect (GenAI / MLOps) Position Overview Turning promising AI ideas into production systems that are secure, compliant, and built to scale is the hard part of enterprise AI. This role sits at the center of that work. You'll own the architecture for enterprise AI and GenAI solutions end to end, from data foundations and model selection through deployment, governance, and monitoring. You'll work closely with business, data, engineering, and risk teams, and you'll advise executives on what's feasible, what's worth it, and what the trade-offs are. What You'll Do / Key Responsibilities Design end-to-end AI/ML and GenAI architectures (RAG, agents, fine-tuning, model serving) that integrate with existing enterprise platforms Evaluate and select LLMs, vector databases, orchestration frameworks, and MLOps tooling Define reference architectures, patterns, and standards for reuse across teams Partner with data engineering on pipelines, feature stores, and data quality for AI workloads Build in responsible AI: model risk management, explainability, bias testing, audit trails, and regulatory compliance Establish MLOps/LLMOps practices covering CI/CD, evaluation, observability, drift detection, and cost control Lead proofs of concept and pilots, and move them to production Advise stakeholders and executives on AI strategy, feasibility, and trade-offs Mentor engineers and data scientists, and run architecture reviews Required Qualifications 10+ years in software or data engineering, with 4+ years architecting AI/ML solutions in production Hands-on experience with LLMs and GenAI: prompt engineering, RAG, embeddings, and agent frameworks (e.g., LangChain, LlamaIndex) Strong Python skills and fluency in ML frameworks (PyTorch, TensorFlow, scikit-learn) Deep experience with at least one major cloud (AWS, Azure, or GCP) and its AI services (e.g., SageMaker, Bedrock, Azure OpenAI, Vertex AI) Experience with MLOps tooling (MLflow, Kubeflow, Docker, Kubernetes, CI/CD) Solid grounding in data architecture: data lakes/warehouses, streaming, APIs, and microservices Knowledge of security, privacy, and governance for AI systems Excellent communication, with the ability to explain technical trade-offs to non-technical leaders Preferred Qualifications Experience in banking, insurance, or asset/wealth management, including regulatory expectations (e.g., SR 11-7 model risk) Experience with vector databases (Pinecone, pgvector, OpenSearch) and fine-tuning or evaluation frameworks Cloud or architecture certifications (e.g., AWS ML Specialty, Azure AI Engineer, TOGAF) Master's or PhD in Computer Science, Data Science, or a related field What Makes HTC A Great Place To Build Your Future HTC Global Services wants you to join our team. Come build new things with us and advance your career. At HTC Global, you'll collaborate with experts, work alongside clients, and be part of high-performing teams driving success together. You'll have long-term opportunities to grow your career and develop skills in the latest emerging technologies. At HTC Global Services, our employees have access to a comprehensive benefits package. Benefits can include Group Health (Medical, Dental, and Vision), Paid Time Off, Paid Holidays, 401(k) matching, Group Life and Disability insurance, Professional Development opportunities, Wellness programs, and a variety of other perks. Our success as a company is built on inclusion and diversity. HTC Global Services is committed to providing a workplace free from discrimination and harassment, where every employee is treated with dignity and respect. We celebrate differences and believe that diverse cultures, perspectives, and skills drive innovation and success. HTC is an Equal Opportunity Employer and a proud National Minority Supplier. We seek to empower each individual, fostering an environment where everyone feels valued, included, and respected.
This job is with Allianz Commercial, an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. We are looking for an experienced Principal Data Engineer to work hands-on on the re-engineering of an existing enterprise data platform built on Azure Synapse Analytics. The role requires strong technical depth to audit, understand, and validate a complex end-to-end data architecture spanning source ingestion through to consumption - and to help deliver the migration of validated workloads to Databricks. This is hands on role and demands the ability to reverse-engineer existing implementations, assess their correctness, and build to an agreed migration strategy. Key Responsibilities Contribute to the technical assessment and re-engineering of an existing enterprise data platform, spanning all layers from source ingestion through to data consumption Reverse-engineer, document, and validate existing pipeline logic, data models, transformation frameworks, and data governance controls Identify gaps, defects, and technical debt across the platform and remediate where implementations are incorrect or sub-optimal Ensure correctness of data processing patterns including change data capture, slowly changing dimensions, deduplication, and business reconciliation Implement target-state designs aligned to modern lakehouse principles, ensuring feature parity and business logic fidelity during transitions Support platform evolution initiatives, including parallel-run phases where multiple implementations operate simultaneously, validating output consistency before cutover Execute migration of existing workloads to modern data platforms, preserving existing governance and control framework semantics Re-implement ingestion, transformation, and orchestration pipelines on target platforms, maintaining audit, quality, and reconciliation standards Collaborate with business, data governance, and architecture stakeholders to validate embedded business rules and data quality requirements Mentor less experienced engineers, review code and designs, and support decommission planning for legacy components Core Technical Skills Azure Synapse & Data Platform Mandatory hands-on expertise with: Azure Synapse Analytics (Pipelines, Spark Pool, Dedicated SQL Pool) Azure Data Lake Storage Gen2 (ADLS Gen2) Delta Lake on Azure (Synapse Lakehouse patterns) Oracle Golden Gate Replication for real-time source integration Azure Analysis Services and Power BI consumption layer patterns Deep understanding of medallion architecture: Raw / Harmonized / Conformed / Consumption layers Strong knowledge of SCD Type 0/1/2, CDC patterns, soft/hard delete, and retroactive change processing Experience with Synapse SQL Pool - stored procedures, control tables, and data quality validation patterns Experience with audit, balance, and control frameworks - parameterized, modular pipeline governance at enterprise scale Familiarity with config-driven and automation-first pipeline patterns (YAML, PySpark, SQL-driven generation from mapping documents) Databricks & Lakehouse Hands-on experience with Azure Databricks (Delta Live Tables, Unity Catalog preferred) Strong Apache Spark skills (PySpark / Spark SQL) Experience migrating workloads from legacy data warehouse or Synapse environments to a Databricks Lakehouse Ability to re-implement governance and control frameworks natively in Databricks (audit logging, reconciliation, DQ checks) Experience with Delta Lake features: MERGE, CDC, time travel, schema enforcement Data Engineering & Development Strong Python and SQL programming skills Experience with ETL/ELT at scale: denormalization, surrogate keys, directory tables, curated data models Experience integrating complex data sources: Oracle DB, SQL Server, Azure SQL DB, file systems, Salesforce, APIs Strong data modelling skills: relational, dimensional, and lakehouse-oriented DevOps & Automation CI/CD pipelines for data engineering (Azure DevOps / GitHub Actions) Infrastructure as Code (Terraform or ARM) Containerization (Docker) Experience with automated testing frameworks for data pipelines (unit testing, reconciliation-based validation) Nice to Have Experience with Unity Catalog for data governance and lineage Familiarity with Azure Purview for data cataloguing and governance Exposure to real-time and streaming pipelines (Event Hub / Kafka / Kinesis) Experience with GenAI or ML platform integration (MLOps, feature engineering pipelines) Familiarity with monitoring and observability tools (e.g., Dynatrace) Exposure to BI tools (Power BI, Tableau) Experience & Profile 7+ years of hands-on experience in Data Engineering, including platform migration or re-engineering work Proven track record working on existing, complex enterprise data platforms - not just building from scratch Deep knowledge of enterprise data governance patterns: audit trails, reconciliation, data quality controls, SCD versioning Strong analytical mindset: ability to read existing implementations, identify intent versus defect, and make sound re-engineering decisions Primarily hands-on, while comfortable contributing to technical design discussions Strong communication skills - able to engage business, governance, and engineering stakeholders with clarity Experience working in regulated or enterprise-scale environments (financial services a plus) What we offer We offer a hybrid work model which recognizes the value of striking a balance between in-person collaboration and remote working We believe in rewarding performance and our compensation and benefits package includes health insurance, company bonus scheme, 401(K) with company match, company paid holidays, paid time off, paid volunteer days, tuition reimbursement, paid parental leave, an employee shares program and employee discounts From career development and digital learning programs to international career mobility, we offer lifelong learning for our employees worldwide and an environment where innovation, delivery and empowerment are fostered. The annualized base pay range for this role is $110,000 - 150,000. The annual base salary range represents a nationwide market range. The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role, and the skills, education, training, credentials and experience of the candidate. The base pay is just one component of the ATA total compensation package. As part of our comprehensive compensation and highly rated benefits programs, ATA also offers eligibility for an incentive-based annual bonus. About Allianz Technology Allianz Group is one of the most trusted insurance and asset management companies in the world. Caring for our employees, their ambitions, dreams and challenges is what makes us a unique employer. We are united by a shared commitment: to put our customers first and at the centre of everything we do. Their needs inspire our thinking and guide our actions. Together, we can build an environment where everyone feels empowered and confident to explore, grow and shape a better future - for our customers and for the world around us. At Allianz, we stand for unity: we believe that a united world is a more prosperous world, and we are dedicated to consistently advocating for equal opportunities for all. The foundation for this is our inclusive workplace, where people and performance both matter, and where integrity, fairness, inclusion and trust are at the heart of our culture. We therefore welcome applications regardless of ethnicity or cultural background, age, gender, nationality, religion, social class, disability or sexual orientation, or any other characteristics protected under applicable local laws and regulations. Join us. Let's care for tomorrow. You.IT
10/27/2026
Full time
This job is with Allianz Commercial, an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. We are looking for an experienced Principal Data Engineer to work hands-on on the re-engineering of an existing enterprise data platform built on Azure Synapse Analytics. The role requires strong technical depth to audit, understand, and validate a complex end-to-end data architecture spanning source ingestion through to consumption - and to help deliver the migration of validated workloads to Databricks. This is hands on role and demands the ability to reverse-engineer existing implementations, assess their correctness, and build to an agreed migration strategy. Key Responsibilities Contribute to the technical assessment and re-engineering of an existing enterprise data platform, spanning all layers from source ingestion through to data consumption Reverse-engineer, document, and validate existing pipeline logic, data models, transformation frameworks, and data governance controls Identify gaps, defects, and technical debt across the platform and remediate where implementations are incorrect or sub-optimal Ensure correctness of data processing patterns including change data capture, slowly changing dimensions, deduplication, and business reconciliation Implement target-state designs aligned to modern lakehouse principles, ensuring feature parity and business logic fidelity during transitions Support platform evolution initiatives, including parallel-run phases where multiple implementations operate simultaneously, validating output consistency before cutover Execute migration of existing workloads to modern data platforms, preserving existing governance and control framework semantics Re-implement ingestion, transformation, and orchestration pipelines on target platforms, maintaining audit, quality, and reconciliation standards Collaborate with business, data governance, and architecture stakeholders to validate embedded business rules and data quality requirements Mentor less experienced engineers, review code and designs, and support decommission planning for legacy components Core Technical Skills Azure Synapse & Data Platform Mandatory hands-on expertise with: Azure Synapse Analytics (Pipelines, Spark Pool, Dedicated SQL Pool) Azure Data Lake Storage Gen2 (ADLS Gen2) Delta Lake on Azure (Synapse Lakehouse patterns) Oracle Golden Gate Replication for real-time source integration Azure Analysis Services and Power BI consumption layer patterns Deep understanding of medallion architecture: Raw / Harmonized / Conformed / Consumption layers Strong knowledge of SCD Type 0/1/2, CDC patterns, soft/hard delete, and retroactive change processing Experience with Synapse SQL Pool - stored procedures, control tables, and data quality validation patterns Experience with audit, balance, and control frameworks - parameterized, modular pipeline governance at enterprise scale Familiarity with config-driven and automation-first pipeline patterns (YAML, PySpark, SQL-driven generation from mapping documents) Databricks & Lakehouse Hands-on experience with Azure Databricks (Delta Live Tables, Unity Catalog preferred) Strong Apache Spark skills (PySpark / Spark SQL) Experience migrating workloads from legacy data warehouse or Synapse environments to a Databricks Lakehouse Ability to re-implement governance and control frameworks natively in Databricks (audit logging, reconciliation, DQ checks) Experience with Delta Lake features: MERGE, CDC, time travel, schema enforcement Data Engineering & Development Strong Python and SQL programming skills Experience with ETL/ELT at scale: denormalization, surrogate keys, directory tables, curated data models Experience integrating complex data sources: Oracle DB, SQL Server, Azure SQL DB, file systems, Salesforce, APIs Strong data modelling skills: relational, dimensional, and lakehouse-oriented DevOps & Automation CI/CD pipelines for data engineering (Azure DevOps / GitHub Actions) Infrastructure as Code (Terraform or ARM) Containerization (Docker) Experience with automated testing frameworks for data pipelines (unit testing, reconciliation-based validation) Nice to Have Experience with Unity Catalog for data governance and lineage Familiarity with Azure Purview for data cataloguing and governance Exposure to real-time and streaming pipelines (Event Hub / Kafka / Kinesis) Experience with GenAI or ML platform integration (MLOps, feature engineering pipelines) Familiarity with monitoring and observability tools (e.g., Dynatrace) Exposure to BI tools (Power BI, Tableau) Experience & Profile 7+ years of hands-on experience in Data Engineering, including platform migration or re-engineering work Proven track record working on existing, complex enterprise data platforms - not just building from scratch Deep knowledge of enterprise data governance patterns: audit trails, reconciliation, data quality controls, SCD versioning Strong analytical mindset: ability to read existing implementations, identify intent versus defect, and make sound re-engineering decisions Primarily hands-on, while comfortable contributing to technical design discussions Strong communication skills - able to engage business, governance, and engineering stakeholders with clarity Experience working in regulated or enterprise-scale environments (financial services a plus) What we offer We offer a hybrid work model which recognizes the value of striking a balance between in-person collaboration and remote working We believe in rewarding performance and our compensation and benefits package includes health insurance, company bonus scheme, 401(K) with company match, company paid holidays, paid time off, paid volunteer days, tuition reimbursement, paid parental leave, an employee shares program and employee discounts From career development and digital learning programs to international career mobility, we offer lifelong learning for our employees worldwide and an environment where innovation, delivery and empowerment are fostered. The annualized base pay range for this role is $110,000 - 150,000. The annual base salary range represents a nationwide market range. The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role, and the skills, education, training, credentials and experience of the candidate. The base pay is just one component of the ATA total compensation package. As part of our comprehensive compensation and highly rated benefits programs, ATA also offers eligibility for an incentive-based annual bonus. About Allianz Technology Allianz Group is one of the most trusted insurance and asset management companies in the world. Caring for our employees, their ambitions, dreams and challenges is what makes us a unique employer. We are united by a shared commitment: to put our customers first and at the centre of everything we do. Their needs inspire our thinking and guide our actions. Together, we can build an environment where everyone feels empowered and confident to explore, grow and shape a better future - for our customers and for the world around us. At Allianz, we stand for unity: we believe that a united world is a more prosperous world, and we are dedicated to consistently advocating for equal opportunities for all. The foundation for this is our inclusive workplace, where people and performance both matter, and where integrity, fairness, inclusion and trust are at the heart of our culture. We therefore welcome applications regardless of ethnicity or cultural background, age, gender, nationality, religion, social class, disability or sexual orientation, or any other characteristics protected under applicable local laws and regulations. Join us. Let's care for tomorrow. You.IT
Job Description Job Description Company Description About AbbVie AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology, and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at . on LinkedIn, Facebook, Instagram, X and YouTube. Job Description The Director, Data & AI Architect, Operations and Finance will define and lead the overall Data & AI architecture strategy for Operations and Finance, ensuring alignment with enterprise priorities and business transformation goals. This role will provide senior architecture leadership across Data & AI technology capabilities, helping translate complex business needs into scalable, secure, and reusable technology patterns. The leader will partner closely with business, technology, enterprise architecture, security, compliance, and vendor teams to shape target-state architecture, guide investment decisions, and enable trusted, high-value Data & AI solutions across the Operations and Finance portfolio. Key Responsibilities Own the Operations & Finance Data & AI architecture vision, strategy, and multi-year architecture roadmap; align architecture with data & AI enterprise standards, business strategy, regulatory requirements, and technology investment priorities. Define and establish the target operating model for Data and AI architecture across Operations and Finance, including governance, decision rights, architecture review mechanisms, reusable patterns, and clear accountability across business and technology organizations. Lead and develop a high-performing leadership bench of senior architects; build organizational capability, succession depth, and a scalable architecture community of practice in collaboration with enterprise architects. Guide enterprise tooling and partner strategy for data and AI; evaluate strategic vendors, influence sourcing decisions, and oversee complex proof-of-concept and due diligence efforts tied to long-term capability building. Define architecture patterns for consuming trusted enterprise data products, semantic services, knowledge graphs, and retrieval services to enable scalable AI-powered business applications. Contribute to financial stewardship for a large-scale architecture and transformation portfolio, including shaping annual and multi-year investment plans, cost discipline, and value realization expectations. Establish architecture standards for Data & AI platform and applications, including data platform, data products, MLOps, agentic orchestration, multi-agent collaboration, human-in-the-loop controls, memory management, runtime observability, guardrails, and responsible AI patterns. Define runtime architecture standards for scalability, resiliency, performance, monitoring, telemetry, observability, disaster recovery, and operational readiness of Data & AI applications. Qualifications Required: Bachelor's + 10 years, Master's + 9 years, or PhD + 5 years of relevant experience. Proven experience defining roadmaps, target architecture, and modernization investment strategies. Experience leading architecture teams, mentoring architects, and scaling architecture practices. Deep expertise in cloud-native, lakehouse, event-driven, microservices, API-first, and GenAI application architectures. Experience establishing architecture governance, review boards, standards, and operating models. Strong ability to influence executives by connecting technology decisions to business value and risk. Preferred: Experience with SAP platforms (S/4HANA, BW, BPC, Datasphere) and SAP-integrated Data & AI solutions. Knowledge of UI/UX patterns for Data & AI applications, including agent-assisted experiences. Familiarity with model selection, fine-tuning, optimization, and cost-performance tradeoffs. Experience in regulated industries with strong security, compliance, and quality requirements. Additional Information Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future. We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees. This job is eligible to participate in our long-term incentive programs. Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company's sole and absolute discretion, consistent with applicable law. AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled. US & Puerto Rico only - to learn more, visit -us/equal-employment-opportunity-employer.html US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more: -us/reasonable- accommodations.html
10/26/2026
Full time
Job Description Job Description Company Description About AbbVie AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology, and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at . on LinkedIn, Facebook, Instagram, X and YouTube. Job Description The Director, Data & AI Architect, Operations and Finance will define and lead the overall Data & AI architecture strategy for Operations and Finance, ensuring alignment with enterprise priorities and business transformation goals. This role will provide senior architecture leadership across Data & AI technology capabilities, helping translate complex business needs into scalable, secure, and reusable technology patterns. The leader will partner closely with business, technology, enterprise architecture, security, compliance, and vendor teams to shape target-state architecture, guide investment decisions, and enable trusted, high-value Data & AI solutions across the Operations and Finance portfolio. Key Responsibilities Own the Operations & Finance Data & AI architecture vision, strategy, and multi-year architecture roadmap; align architecture with data & AI enterprise standards, business strategy, regulatory requirements, and technology investment priorities. Define and establish the target operating model for Data and AI architecture across Operations and Finance, including governance, decision rights, architecture review mechanisms, reusable patterns, and clear accountability across business and technology organizations. Lead and develop a high-performing leadership bench of senior architects; build organizational capability, succession depth, and a scalable architecture community of practice in collaboration with enterprise architects. Guide enterprise tooling and partner strategy for data and AI; evaluate strategic vendors, influence sourcing decisions, and oversee complex proof-of-concept and due diligence efforts tied to long-term capability building. Define architecture patterns for consuming trusted enterprise data products, semantic services, knowledge graphs, and retrieval services to enable scalable AI-powered business applications. Contribute to financial stewardship for a large-scale architecture and transformation portfolio, including shaping annual and multi-year investment plans, cost discipline, and value realization expectations. Establish architecture standards for Data & AI platform and applications, including data platform, data products, MLOps, agentic orchestration, multi-agent collaboration, human-in-the-loop controls, memory management, runtime observability, guardrails, and responsible AI patterns. Define runtime architecture standards for scalability, resiliency, performance, monitoring, telemetry, observability, disaster recovery, and operational readiness of Data & AI applications. Qualifications Required: Bachelor's + 10 years, Master's + 9 years, or PhD + 5 years of relevant experience. Proven experience defining roadmaps, target architecture, and modernization investment strategies. Experience leading architecture teams, mentoring architects, and scaling architecture practices. Deep expertise in cloud-native, lakehouse, event-driven, microservices, API-first, and GenAI application architectures. Experience establishing architecture governance, review boards, standards, and operating models. Strong ability to influence executives by connecting technology decisions to business value and risk. Preferred: Experience with SAP platforms (S/4HANA, BW, BPC, Datasphere) and SAP-integrated Data & AI solutions. Knowledge of UI/UX patterns for Data & AI applications, including agent-assisted experiences. Familiarity with model selection, fine-tuning, optimization, and cost-performance tradeoffs. Experience in regulated industries with strong security, compliance, and quality requirements. Additional Information Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future. We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees. This job is eligible to participate in our long-term incentive programs. Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company's sole and absolute discretion, consistent with applicable law. AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled. US & Puerto Rico only - to learn more, visit -us/equal-employment-opportunity-employer.html US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more: -us/reasonable- accommodations.html
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 17,000+, and has 58 offices in 22 countries within key global markets. Our challenge: We are seeking an experienced GenAI Architect to lead the design, development, and implementation of enterprise-scale Generative AI solutions. The ideal candidate will possess deep expertise in Large Language Models (LLMs), Agentic AI frameworks, Retrieval-Augmented Generation (RAG), AI governance, cloud platforms, and modern software architecture principles. This role will collaborate with business stakeholders, engineering teams, data scientists, and product owners to drive AI transformation initiatives and deliver scalable, secure, and production-ready 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 $120k - $130k/year & benefits (see below). The Role Responsibilities: Define enterprise GenAI strategy, architecture standards, and AI adoption roadmaps. Design and implement end-to-end GenAI solutions leveraging LLMs, RAG, AI Agents, and multi-agent systems. Architect scalable AI applications using OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, Llama, Mistral, and other foundation models. Lead the design and implementation of AI agent frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, and ADK. Design knowledge retrieval architectures using vector databases and semantic search technologies. Develop AI governance frameworks covering security, compliance, observability, responsible AI, and model evaluation. Architect solutions for prompt engineering, model fine-tuning, grounding, and optimization. Collaborate with engineering teams to establish CI/CD, MLOps, and LLMOps best practices. Drive solution reviews, POCs, and production deployments across business functions. Provide technical leadership and mentorship to AI Engineers, Data Scientists, and Architects. Evaluate emerging AI technologies and recommend enterprise adoption strategies. Partner with executive leadership to identify business opportunities through GenAI innovation. Required Skills: GenAI & LLM Technologies Large Language Models (GPT, Claude, Gemini, Llama, Mistral) Prompt Engineering and Prompt Optimization Retrieval-Augmented Generation (RAG) Fine-Tuning and PEFT techniques AI Agents and Multi-Agent Architectures Function Calling and Tool Integration Model Evaluation and Benchmarking Agentic AI Frameworks LangChain LangGraph CrewAI AutoGen Microsoft Semantic Kernel Google Agent Development Kit (ADK) OpenAI Agents SDK Vector Databases Pinecone Weaviate ChromaDB Qdrant Azure AI Search Elasticsearch/OpenSearch Cloud Platforms Microsoft Azure Azure OpenAI Services AWS Bedrock Google Vertex AI Databricks AI Platform Programming & Development Python FastAPI REST APIs Microservices Architecture Java (Preferred) Node.js (Good to Have) Data & AI Engineering SQL / NoSQL Databases Data Pipelines Data Governance Knowledge Graphs Embedding Models DevOps / MLOps Docker Kubernetes GitHub Actions Azure DevOps MLflow LangSmith Monitoring & Observability Tools Preferred, but not required: Define AI reference architectures and reusable design patterns. Establish security controls for AI systems. Create standards for AI application scalability, resiliency, and performance. Design integration patterns between GenAI platforms and enterprise applications. Ensure compliance with Responsible AI and regulatory guidelines. Support architecture review boards and executive steering committees. We offer: A highly competitive compensation and benefits package. A multinational organization with 58 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. SYNECHRON'S DIVERSITY & INCLUSION STATEMENT Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative 'Same Difference' is committed to fostering an inclusive culture - promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more. All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant's gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
10/25/2026
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 17,000+, and has 58 offices in 22 countries within key global markets. Our challenge: We are seeking an experienced GenAI Architect to lead the design, development, and implementation of enterprise-scale Generative AI solutions. The ideal candidate will possess deep expertise in Large Language Models (LLMs), Agentic AI frameworks, Retrieval-Augmented Generation (RAG), AI governance, cloud platforms, and modern software architecture principles. This role will collaborate with business stakeholders, engineering teams, data scientists, and product owners to drive AI transformation initiatives and deliver scalable, secure, and production-ready 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 $120k - $130k/year & benefits (see below). The Role Responsibilities: Define enterprise GenAI strategy, architecture standards, and AI adoption roadmaps. Design and implement end-to-end GenAI solutions leveraging LLMs, RAG, AI Agents, and multi-agent systems. Architect scalable AI applications using OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, Llama, Mistral, and other foundation models. Lead the design and implementation of AI agent frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, and ADK. Design knowledge retrieval architectures using vector databases and semantic search technologies. Develop AI governance frameworks covering security, compliance, observability, responsible AI, and model evaluation. Architect solutions for prompt engineering, model fine-tuning, grounding, and optimization. Collaborate with engineering teams to establish CI/CD, MLOps, and LLMOps best practices. Drive solution reviews, POCs, and production deployments across business functions. Provide technical leadership and mentorship to AI Engineers, Data Scientists, and Architects. Evaluate emerging AI technologies and recommend enterprise adoption strategies. Partner with executive leadership to identify business opportunities through GenAI innovation. Required Skills: GenAI & LLM Technologies Large Language Models (GPT, Claude, Gemini, Llama, Mistral) Prompt Engineering and Prompt Optimization Retrieval-Augmented Generation (RAG) Fine-Tuning and PEFT techniques AI Agents and Multi-Agent Architectures Function Calling and Tool Integration Model Evaluation and Benchmarking Agentic AI Frameworks LangChain LangGraph CrewAI AutoGen Microsoft Semantic Kernel Google Agent Development Kit (ADK) OpenAI Agents SDK Vector Databases Pinecone Weaviate ChromaDB Qdrant Azure AI Search Elasticsearch/OpenSearch Cloud Platforms Microsoft Azure Azure OpenAI Services AWS Bedrock Google Vertex AI Databricks AI Platform Programming & Development Python FastAPI REST APIs Microservices Architecture Java (Preferred) Node.js (Good to Have) Data & AI Engineering SQL / NoSQL Databases Data Pipelines Data Governance Knowledge Graphs Embedding Models DevOps / MLOps Docker Kubernetes GitHub Actions Azure DevOps MLflow LangSmith Monitoring & Observability Tools Preferred, but not required: Define AI reference architectures and reusable design patterns. Establish security controls for AI systems. Create standards for AI application scalability, resiliency, and performance. Design integration patterns between GenAI platforms and enterprise applications. Ensure compliance with Responsible AI and regulatory guidelines. Support architecture review boards and executive steering committees. We offer: A highly competitive compensation and benefits package. A multinational organization with 58 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. SYNECHRON'S DIVERSITY & INCLUSION STATEMENT Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative 'Same Difference' is committed to fostering an inclusive culture - promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more. All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant's gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
Overview Join a team where innovation meets mission. Our AI, cloud, cyber, and modernization solutions save agencies thousands of hours, safeguard national security, and strengthen health and humanitarian missions worldwide. With 1,700+ team members, 1,500+ AI/data experts, and 100+ prime contracts, we deliver at scale and with purpose. We've been recognized as a Top Workplace by the Washington Post for six straight years and named to the Inc. 5000 Fastest Growing Private Companies 13 of the past 14 years. Credence is a welcoming home for those looking to grow and contribute to positive change. We encourage all employees to expand beyond their boundaries, dive into important world-changing Federal challenges. Position Summary We have an immediate need for a highly skilled Senior Google AI Engineer. We are growing our Google Cloud AI engineering capability to support our Department of War (DoW) programs. You will design, build, and operationalize production grade AI systems on Google Cloud-accelerating mission outcomes for a high visibility program. As a Senior Google AI Engineer, you will serve as a hands on technical leader for AI solution delivery. You'll translate mission needs into secure, scalable AI/ML systems; guide data, platform, and application engineers; and ensure solutions meet DoW security and compliance requirements in production. The ideal candidate combines deep GCP/Looker/BigQuery/Vertex AI expertise with strong MLOps, data engineering fluency, and experience delivering in regulated environments. Responsibilities include, but are not limited to the duties listed below: Architect and deliver end to end AI/ML solutions on Google Cloud using Vertex AI (Workbench, Pipelines, Training, Model Registry, Online/Batch Prediction, Feature Store, Model Monitoring) and Gemini/LLM services-optimized for performance, cost, and maintainability. Develop production data pipelines with BigQuery, Dataflow, and Dataproc; integrate streaming via Pub/Sub; containerize and orchestrate with Cloud Run and GKE; automate CI/CD with Cloud Build and IaC. Implement robust MLOps (experiment tracking, evaluation, bias/robustness testing, model versioning, canary/blue green rollouts, automated retraining, drift detection, and lineage). Apply secure by design patterns-VPC SC, private service access, CMEK, fine grained IAM, artifact signing, and secrets management-aligned to NIST , RMF, and FedRAMP baselines. Operationalize LLM/GenAI (RAG, tool use/agents, safety filters, evaluation harnesses) including retrieval over structured/unstructured data; leverage DoW approved AI toolchains where appropriate. Partner with mission stakeholders to elicit requirements, frame measurable success criteria, and deliver iterative value; provide technical mentorship and lead design/code reviews for engineering teams. Contribute to program roadmaps (including use cases), documenting architectures, controls, and SOPs for sustained operations. Requirements US citizenship with the ability to obtain successful DoW Secret security clearance required. Candidates with active Secret clearance preferred. Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field. Advanced degree (Master's Degree or PhD) in AI/ML, Data Science, Computer Science, or a closely related discipline is preferred but not required, especially when balanced with substantial hands on experience (5+ years) in AI/ML solution development. 8+ years total software development/data experience with 5+ years focused on AI/ML engineering and MLOps, including production deployments on Google Cloud (GCP). Proven experience architecting robust data ingestion, processing, and transformation workflows using Dataflow, Data Fusion, Dataproc, BigQuery, and Looker and integrating these platforms with Vertex AI for model training, deployment, and inference. Must possess a current Google Associate Cloud Engineer (GCP-ACE) or Google Professional Cloud Architect (GCP-PCA) certification or be able to obtain it within 90 days of hire. Must possess at least one current DoW Cyber Baseline Certification (e.g., Security+ Intermediate, SecurityX/CASP+ Advanced) or be able to obtain it within 90 days of hire. Proven experience with extracting data from SAP Enterprise Business Applications. Expert in Python (and/or Go/TypeScript) for AI services; strong with Vertex AI, BigQuery, Dataflow and/or Data Fusion, GKE/Cloud Run, Cloud Build, Cloud Storage, Pub/Sub. Practical experience with LLMs/GenAI (e.g., Gemini), vector databases, prompt engineering, RAG patterns, and evaluation/guardrails. Proven MLOps track record (Pipelines, CI/CD for ML, feature stores, monitoring/drift, automated retraining) and strong data engineering fundamentals. Ability to design for security & compliance in DoW/Federal contexts (NIST , RMF, FedRAMP; Zero Trust principles). Hands on experience with Vertex AI Agent Builder, Model Garden, embeddings/vector search (e.g., BigQuery Vector, AlloyDB AI), and evaluation frameworks. Experience integrating GenAI safely within IL5 environments and familiarity with available government AI platforms. Salary Range: $150,000 - $190,000 annually. Actual compensation will be determined based on the selected candidate's experience, education, certifications, skills, and overall qualifications. Benefits Health Care Plan (Medical, Dental & Vision) Retirement Plan (401k, IRA) Life Insurance (Basic, Voluntary & AD&D) Paid Time Off (Vacation, Sick & Public Holidays) Family Leave (Maternity, Paternity) Short Term & Long Term Disability Training & Development Wellness Resources
10/24/2026
Full time
Overview Join a team where innovation meets mission. Our AI, cloud, cyber, and modernization solutions save agencies thousands of hours, safeguard national security, and strengthen health and humanitarian missions worldwide. With 1,700+ team members, 1,500+ AI/data experts, and 100+ prime contracts, we deliver at scale and with purpose. We've been recognized as a Top Workplace by the Washington Post for six straight years and named to the Inc. 5000 Fastest Growing Private Companies 13 of the past 14 years. Credence is a welcoming home for those looking to grow and contribute to positive change. We encourage all employees to expand beyond their boundaries, dive into important world-changing Federal challenges. Position Summary We have an immediate need for a highly skilled Senior Google AI Engineer. We are growing our Google Cloud AI engineering capability to support our Department of War (DoW) programs. You will design, build, and operationalize production grade AI systems on Google Cloud-accelerating mission outcomes for a high visibility program. As a Senior Google AI Engineer, you will serve as a hands on technical leader for AI solution delivery. You'll translate mission needs into secure, scalable AI/ML systems; guide data, platform, and application engineers; and ensure solutions meet DoW security and compliance requirements in production. The ideal candidate combines deep GCP/Looker/BigQuery/Vertex AI expertise with strong MLOps, data engineering fluency, and experience delivering in regulated environments. Responsibilities include, but are not limited to the duties listed below: Architect and deliver end to end AI/ML solutions on Google Cloud using Vertex AI (Workbench, Pipelines, Training, Model Registry, Online/Batch Prediction, Feature Store, Model Monitoring) and Gemini/LLM services-optimized for performance, cost, and maintainability. Develop production data pipelines with BigQuery, Dataflow, and Dataproc; integrate streaming via Pub/Sub; containerize and orchestrate with Cloud Run and GKE; automate CI/CD with Cloud Build and IaC. Implement robust MLOps (experiment tracking, evaluation, bias/robustness testing, model versioning, canary/blue green rollouts, automated retraining, drift detection, and lineage). Apply secure by design patterns-VPC SC, private service access, CMEK, fine grained IAM, artifact signing, and secrets management-aligned to NIST , RMF, and FedRAMP baselines. Operationalize LLM/GenAI (RAG, tool use/agents, safety filters, evaluation harnesses) including retrieval over structured/unstructured data; leverage DoW approved AI toolchains where appropriate. Partner with mission stakeholders to elicit requirements, frame measurable success criteria, and deliver iterative value; provide technical mentorship and lead design/code reviews for engineering teams. Contribute to program roadmaps (including use cases), documenting architectures, controls, and SOPs for sustained operations. Requirements US citizenship with the ability to obtain successful DoW Secret security clearance required. Candidates with active Secret clearance preferred. Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field. Advanced degree (Master's Degree or PhD) in AI/ML, Data Science, Computer Science, or a closely related discipline is preferred but not required, especially when balanced with substantial hands on experience (5+ years) in AI/ML solution development. 8+ years total software development/data experience with 5+ years focused on AI/ML engineering and MLOps, including production deployments on Google Cloud (GCP). Proven experience architecting robust data ingestion, processing, and transformation workflows using Dataflow, Data Fusion, Dataproc, BigQuery, and Looker and integrating these platforms with Vertex AI for model training, deployment, and inference. Must possess a current Google Associate Cloud Engineer (GCP-ACE) or Google Professional Cloud Architect (GCP-PCA) certification or be able to obtain it within 90 days of hire. Must possess at least one current DoW Cyber Baseline Certification (e.g., Security+ Intermediate, SecurityX/CASP+ Advanced) or be able to obtain it within 90 days of hire. Proven experience with extracting data from SAP Enterprise Business Applications. Expert in Python (and/or Go/TypeScript) for AI services; strong with Vertex AI, BigQuery, Dataflow and/or Data Fusion, GKE/Cloud Run, Cloud Build, Cloud Storage, Pub/Sub. Practical experience with LLMs/GenAI (e.g., Gemini), vector databases, prompt engineering, RAG patterns, and evaluation/guardrails. Proven MLOps track record (Pipelines, CI/CD for ML, feature stores, monitoring/drift, automated retraining) and strong data engineering fundamentals. Ability to design for security & compliance in DoW/Federal contexts (NIST , RMF, FedRAMP; Zero Trust principles). Hands on experience with Vertex AI Agent Builder, Model Garden, embeddings/vector search (e.g., BigQuery Vector, AlloyDB AI), and evaluation frameworks. Experience integrating GenAI safely within IL5 environments and familiarity with available government AI platforms. Salary Range: $150,000 - $190,000 annually. Actual compensation will be determined based on the selected candidate's experience, education, certifications, skills, and overall qualifications. Benefits Health Care Plan (Medical, Dental & Vision) Retirement Plan (401k, IRA) Life Insurance (Basic, Voluntary & AD&D) Paid Time Off (Vacation, Sick & Public Holidays) Family Leave (Maternity, Paternity) Short Term & Long Term Disability Training & Development Wellness Resources
What Makes a Honda, is Who makes a Honda Honda has a clear vision for the future, and it's a joyful one. We are looking for individuals with the skills, courage, persistence, and dreams that will help us reach our future-focused goals. At our core is innovation. Honda is constantly innovating and developing solutions to drive our business with record success. We strive to be a company that serves as a source of "power" that supports people around the world who are trying to do things based on their own initiative and that helps people expand their own potential. To this end, Honda strives to realize "the joy and freedom of mobility" by developing new technologies and an innovative approach to achieve a "zero environmental footprint." We are looking for qualified individuals with diverse backgrounds, experiences, continuous improvement values, and a strong work ethic to join our team. If your goals and values align with Honda's, we want you to join our team to Bring the Future! Job Purpose The Data and Solution Architect is responsible for defining, governing, and advancing the enterprise data architecture required to enable scalable, trusted, and responsible AI solutions in the AI Hub. This role ensures that data platforms, models, standards, and semantic structures are designed to support AI/ML, Generative AI, Agentic AI, experimentation (POCs), and production deployment, while aligning with enterprise data governance, security, and compliance requirements. Key Accountabilities AI Ready Data Architecture & Design Lead the design of logical, physical, and semantic data architectures to support AI, advanced analytics, GenAI and Agentic AI use cases Define AI ready data models, feature oriented structures, and analytics ready data assets aligned to enterprise data platforms Ensure architectural alignment between data platforms, AI tooling, and MLOps pipelines Support design and governance of the Foundry Ontology layer as the enterprise semantic backbone for AI use cases Data Standards, Governance & Semantics Work closely with Data Architecture and Governance teams, define and enforce data standards, metadata practices, naming conventions, and master/reference data patterns for AI use cases Establish and maintain semantic consistency (business definitions, critical data elements, feature definitions) to enable explainable and reusable AI Partner with Data Governance, Legal, and Compliance teams to ensure responsible and compliant AI data usage AI Use Case Enablement & POC Support Architect data flows and patterns supporting AI POCs, experimentation environments, and sandbox platforms Guide data sourcing, profiling, and readiness activities for AI Data Scientists, Data and AI Engineers Own the data architecture stage-gate process across POC, expanded pilot, and production phases; define and maintain AI-readiness scorecards to assess data asset maturity at each stage Data Platform, Integration and Solution Architecture Architect integrations across source systems, data platforms, analytics tools, and AI services Evaluate and onboard new data sources and technologies to meet evolving AI requirements Define reference architecture and architectural guardrails for cloud, hybrid, and global deployments Lead/support solution architecture development for projects/products, where necessary Collaboration, Review & Enablement Serve as a data and solution architecture authority for AI related initiatives and design reviews Mentor junior architects, data engineers and provide architectural guidance to delivery teams Contribute to AI Hub (COE), communities of practice, standards bodies, and architecture review forums Qualifications, Experience, and Skills Bachelor's degree in Computer Science, Information Systems, Data Engineering/ Data Science, or related field 8+ years of experience in data architecture, solution architecture, enterprise data platforms, or analytics architecture Demonstrated experience supporting AI/ML, advanced analytics, or data driven product initiatives Experience operating in federated governance or hub and spoke operating models Experience with data profiling and data quality management Workstyle This is an onsite job. One remote workday per week, may be possible with prior departmental approval. Visa sponsorship issues This position is not eligible for work visa sponsorship What differentiates Honda and makes us an employer of choice? Total Rewards: Competitive Base Salary (pay will be based on several variables that include, but not limited to geographic location, work experience, etc.) Regional Bonus (when applicable) Manager Lease Car Program (No Cost - Car, Maintenance, and Insurance included) Industry-leading Benefit Plans (Medical, Dental, Vision, Rx) Paid time off, including vacation, holidays, shutdown Company Paid Short-Term and Long-Term Disability 401K Plan with company match + additional contribution Relocation assistance (if eligible) Career Growth: Advancement Opportunities Career Mobility Education Reimbursement for Continued learning Training and Development Programs Additional Offerings: Lifestyle Account Childcare Reimbursement Account Elder Care Support Tuition Assistance & Student Loan Repayment Wellbeing Program Community Service and Engagement Programs Product Programs Honda is an equal opportunity employer and considers qualified applicants for employment without regard to race, color, creed, religion, national origin, sex, sexual orientation, gender identity and expression, age, disability, veteran status, or any other protected factor.
09/25/2026
Full time
What Makes a Honda, is Who makes a Honda Honda has a clear vision for the future, and it's a joyful one. We are looking for individuals with the skills, courage, persistence, and dreams that will help us reach our future-focused goals. At our core is innovation. Honda is constantly innovating and developing solutions to drive our business with record success. We strive to be a company that serves as a source of "power" that supports people around the world who are trying to do things based on their own initiative and that helps people expand their own potential. To this end, Honda strives to realize "the joy and freedom of mobility" by developing new technologies and an innovative approach to achieve a "zero environmental footprint." We are looking for qualified individuals with diverse backgrounds, experiences, continuous improvement values, and a strong work ethic to join our team. If your goals and values align with Honda's, we want you to join our team to Bring the Future! Job Purpose The Data and Solution Architect is responsible for defining, governing, and advancing the enterprise data architecture required to enable scalable, trusted, and responsible AI solutions in the AI Hub. This role ensures that data platforms, models, standards, and semantic structures are designed to support AI/ML, Generative AI, Agentic AI, experimentation (POCs), and production deployment, while aligning with enterprise data governance, security, and compliance requirements. Key Accountabilities AI Ready Data Architecture & Design Lead the design of logical, physical, and semantic data architectures to support AI, advanced analytics, GenAI and Agentic AI use cases Define AI ready data models, feature oriented structures, and analytics ready data assets aligned to enterprise data platforms Ensure architectural alignment between data platforms, AI tooling, and MLOps pipelines Support design and governance of the Foundry Ontology layer as the enterprise semantic backbone for AI use cases Data Standards, Governance & Semantics Work closely with Data Architecture and Governance teams, define and enforce data standards, metadata practices, naming conventions, and master/reference data patterns for AI use cases Establish and maintain semantic consistency (business definitions, critical data elements, feature definitions) to enable explainable and reusable AI Partner with Data Governance, Legal, and Compliance teams to ensure responsible and compliant AI data usage AI Use Case Enablement & POC Support Architect data flows and patterns supporting AI POCs, experimentation environments, and sandbox platforms Guide data sourcing, profiling, and readiness activities for AI Data Scientists, Data and AI Engineers Own the data architecture stage-gate process across POC, expanded pilot, and production phases; define and maintain AI-readiness scorecards to assess data asset maturity at each stage Data Platform, Integration and Solution Architecture Architect integrations across source systems, data platforms, analytics tools, and AI services Evaluate and onboard new data sources and technologies to meet evolving AI requirements Define reference architecture and architectural guardrails for cloud, hybrid, and global deployments Lead/support solution architecture development for projects/products, where necessary Collaboration, Review & Enablement Serve as a data and solution architecture authority for AI related initiatives and design reviews Mentor junior architects, data engineers and provide architectural guidance to delivery teams Contribute to AI Hub (COE), communities of practice, standards bodies, and architecture review forums Qualifications, Experience, and Skills Bachelor's degree in Computer Science, Information Systems, Data Engineering/ Data Science, or related field 8+ years of experience in data architecture, solution architecture, enterprise data platforms, or analytics architecture Demonstrated experience supporting AI/ML, advanced analytics, or data driven product initiatives Experience operating in federated governance or hub and spoke operating models Experience with data profiling and data quality management Workstyle This is an onsite job. One remote workday per week, may be possible with prior departmental approval. Visa sponsorship issues This position is not eligible for work visa sponsorship What differentiates Honda and makes us an employer of choice? Total Rewards: Competitive Base Salary (pay will be based on several variables that include, but not limited to geographic location, work experience, etc.) Regional Bonus (when applicable) Manager Lease Car Program (No Cost - Car, Maintenance, and Insurance included) Industry-leading Benefit Plans (Medical, Dental, Vision, Rx) Paid time off, including vacation, holidays, shutdown Company Paid Short-Term and Long-Term Disability 401K Plan with company match + additional contribution Relocation assistance (if eligible) Career Growth: Advancement Opportunities Career Mobility Education Reimbursement for Continued learning Training and Development Programs Additional Offerings: Lifestyle Account Childcare Reimbursement Account Elder Care Support Tuition Assistance & Student Loan Repayment Wellbeing Program Community Service and Engagement Programs Product Programs Honda is an equal opportunity employer and considers qualified applicants for employment without regard to race, color, creed, religion, national origin, sex, sexual orientation, gender identity and expression, age, disability, veteran status, or any other protected factor.
Qualcomm seeks a Principal, Gen AI Transformation leader to drive large scale AI initiatives across its Telecommunications & Media portfolio. This role defines the GenAI strategy, partners with product, engineering, and business units, and converts use cases into scalable solutions spanning devices, networks, and cloud. You will lead discovery workshops, design reference architectures, and guide pilots to production, ensuring security, compliance, and performance. Collaborate with global teams to embed AI into workflows, measure business impact, and build an enterprise roadmap that accelerates innovation and competitive advantage. Responsibilities Define and drive Qualcomm's enterprise Gen AI transformation strategy across products and business units Identify, prioritize, and shape high value Gen AI use cases with telecommunications and media stakeholders Design scalable Gen AI architectures spanning device, edge, network, and cloud platforms Lead end to end delivery of Gen AI pilots and scale them to secure, compliant production solutions Collaborate with engineering, product, and operations to embed AI into tools, workflows, and services Establish governance, risk, and performance metrics for Gen AI solutions and model lifecycle Partner with data, security, and IT teams to ensure robust data pipelines and responsible AI practices Evangelize Gen AI capabilities, run workshops, and upskill internal teams on best practices Measure and communicate business impact, creating executive ready roadmaps and progress updates Stay current on AI/ML, LLM, and telecom innovations to inform Qualcomm's technology strategy Required Skills Gen AI and large language models (LLMs) Machine learning and deep learning MLOps and model lifecycle management Cloud platforms (AWS, Azure, or GCP) Data engineering and pipelines Architecture design for scalable AI systems Python or similar programming languages API integration and microservices Model evaluation, monitoring, and observability Security, compliance, and responsible AI practices
09/03/2026
Full time
Qualcomm seeks a Principal, Gen AI Transformation leader to drive large scale AI initiatives across its Telecommunications & Media portfolio. This role defines the GenAI strategy, partners with product, engineering, and business units, and converts use cases into scalable solutions spanning devices, networks, and cloud. You will lead discovery workshops, design reference architectures, and guide pilots to production, ensuring security, compliance, and performance. Collaborate with global teams to embed AI into workflows, measure business impact, and build an enterprise roadmap that accelerates innovation and competitive advantage. Responsibilities Define and drive Qualcomm's enterprise Gen AI transformation strategy across products and business units Identify, prioritize, and shape high value Gen AI use cases with telecommunications and media stakeholders Design scalable Gen AI architectures spanning device, edge, network, and cloud platforms Lead end to end delivery of Gen AI pilots and scale them to secure, compliant production solutions Collaborate with engineering, product, and operations to embed AI into tools, workflows, and services Establish governance, risk, and performance metrics for Gen AI solutions and model lifecycle Partner with data, security, and IT teams to ensure robust data pipelines and responsible AI practices Evangelize Gen AI capabilities, run workshops, and upskill internal teams on best practices Measure and communicate business impact, creating executive ready roadmaps and progress updates Stay current on AI/ML, LLM, and telecom innovations to inform Qualcomm's technology strategy Required Skills Gen AI and large language models (LLMs) Machine learning and deep learning MLOps and model lifecycle management Cloud platforms (AWS, Azure, or GCP) Data engineering and pipelines Architecture design for scalable AI systems Python or similar programming languages API integration and microservices Model evaluation, monitoring, and observability Security, compliance, and responsible AI practices
Qualcomm seeks a Gen AI Transformation Engineer to accelerate embedded software and engineering productivity for next gen wireless and media platforms. You will design and deploy GenAI solutions across firmware, embedded OS, and toolchains to automate workflows, optimize code, and enhance developer experience. Partner with software, silicon, and DevOps teams to integrate AI copilots, code analysis, and test generation into existing pipelines. Evaluate models, build scalable inference services, measure impact on productivity, and drive best practices for secure, compliant AI adoption in a fast paced, innovation driven environment. Responsibilities Design and implement Gen AI solutions to enhance embedded software development and engineering productivity Integrate AI copilots, code generation, and static analysis tools into existing CI/CD and development workflows Collaborate with embedded, firmware, tools, and Dev Ops teams to identify and automate high value engineering use cases Build and maintain scalable Gen AI services and APIs optimized for Qualcomm hardware and software stacks Evaluate and fine tune large language models and domain specific models for code and documentation tasks Develop metrics and dashboards to measure productivity, quality, and performance impact of AI solutions Ensure AI systems follow security, privacy, and responsible AI guidelines and Qualcomm standards Create technical documentation, best practices, and internal training for AI augmented engineering workflows Required Skills Embedded software development C/C++ programming Python programming Machine learning and Gen AI (LLMs) Model integration and APIs (REST/GRPC) CI/CD pipelines and Dev Ops tooling Cloud platforms (AWS, Azure, or GCP) MLOps and model monitoring Software architecture and design Data engineering and feature pipelines
09/03/2026
Full time
Qualcomm seeks a Gen AI Transformation Engineer to accelerate embedded software and engineering productivity for next gen wireless and media platforms. You will design and deploy GenAI solutions across firmware, embedded OS, and toolchains to automate workflows, optimize code, and enhance developer experience. Partner with software, silicon, and DevOps teams to integrate AI copilots, code analysis, and test generation into existing pipelines. Evaluate models, build scalable inference services, measure impact on productivity, and drive best practices for secure, compliant AI adoption in a fast paced, innovation driven environment. Responsibilities Design and implement Gen AI solutions to enhance embedded software development and engineering productivity Integrate AI copilots, code generation, and static analysis tools into existing CI/CD and development workflows Collaborate with embedded, firmware, tools, and Dev Ops teams to identify and automate high value engineering use cases Build and maintain scalable Gen AI services and APIs optimized for Qualcomm hardware and software stacks Evaluate and fine tune large language models and domain specific models for code and documentation tasks Develop metrics and dashboards to measure productivity, quality, and performance impact of AI solutions Ensure AI systems follow security, privacy, and responsible AI guidelines and Qualcomm standards Create technical documentation, best practices, and internal training for AI augmented engineering workflows Required Skills Embedded software development C/C++ programming Python programming Machine learning and Gen AI (LLMs) Model integration and APIs (REST/GRPC) CI/CD pipelines and Dev Ops tooling Cloud platforms (AWS, Azure, or GCP) MLOps and model monitoring Software architecture and design Data engineering and feature pipelines
