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 a