Principal AI Engineer

McKesson•Columbus, OH

About The Position

The Principal AI Engineer provides the highest level of individual-contributor technical leadership for McKesson’s AI platform, operating one level above the Lead AI Engineer role. The position sets multi-year architecture strategy for agentic and LLM-powered systems—including model strategy, retrieval-augmented generation (RAG), tool and connector ecosystems, and evaluation and governance tooling—across all teams building on the platform. In a highly regulated healthcare environment, this engineer resolves the platform’s most difficult and ambiguous technical problems, advises executive leadership on AI strategy and risk, and shapes how the engineering organization adopts AI safely and effectively. Success is measured by the durability and adoption of the technical strategy across teams, the ability to address cross-organizational technical risks before they become delivery or compliance problems, and influence on executive-level AI investment decisions. Priority will be given to candidates who reside in the Columbus, Ohio metropolitan area. McKesson is unable to provide sponsorship for this role, presently or in the future.

Requirements

Bachelor’s degree or equivalent experience; fewer years of experience may be required with a relevant Master’s or Doctorate.
Typically 12+ years of software engineering experience, including 6+ years focused on AI/ML, LLM, or NLP systems in production and 3+ years as a senior individual-contributor technical leader.
Demonstrated technical leadership above the Lead AI Engineer level, setting technical direction and standards across multiple teams.
Proven track record setting technical strategy adopted by multiple teams.
Deep, hands-on expertise building production LLM applications at scale, including prompt engineering, RAG, agentic workflows, tool use, and evaluation harnesses.
Experience with commercial LLM APIs such as Anthropic or OpenAI and tool-integration protocols such as Model Context Protocol (MCP), including evaluation of emerging alternatives.
Strong software engineering fundamentals, including Python and/or Java, REST APIs, event-driven architecture, microservices, and cloud-native design; Azure is preferred, while AWS or GCP is acceptable.
Experience with vector and graph retrieval systems, embeddings, and knowledge-base architectures at production scale across multiple products.
Deep working knowledge of LLMOps/MLOps, including usage attribution, cost and spend monitoring, abuse detection, and model evaluation pipelines, and experience designing these systems from scratch.
Ability to influence and align senior engineering and executive stakeholders without direct authority.
Deep familiarity with regulated or healthcare data environments and AI governance frameworks, including model risk review, connector approval tiers, and PHI handling.
No sponsorship is available for this role, now or in the future.

Nice To Haves

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 in healthcare IT or another highly regulated industry.
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.
Awareness of the fast-moving GenAI and agentic landscape and ability to distinguish 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.

Responsibilities

Own the multi-year technical strategy and roadmap for the AI platform across teams, including model strategy, RAG and retrieval architecture, connector ecosystems, and evaluation frameworks.
Serve as the final technical escalation point for the most difficult and ambiguous AI-platform architecture and reliability problems across multiple teams and systems.
Set, audit, and evolve architecture, security, and evaluation standards adopted by multiple Lead AI Engineers and their teams.
Advise VP- and executive-level leadership on AI platform strategy, model risk, and investment trade-offs.
Personally lead architecture and prototyping for high-ambiguity, high-risk AI initiatives that existing teams or patterns do not address.
Develop Lead and Senior AI Engineers through design reviews, technical coaching, and structured knowledge-sharing across teams.
Shape enterprise-wide AI governance policy, including model risk review, connector and tool approval frameworks, and PHI-handling standards, in partnership with Legal, Compliance, and Privacy leadership.
Own evaluation and long-term strategy for foundation model providers and AI infrastructure vendors across the company.
Represent the company’s AI engineering capability at industry forums, in partner and vendor technical relationships, and in recruiting senior technical talent.
Influence AI platform direction and investment across the broader enterprise, beyond a single business unit.