Principal AI Engineer

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.

…