Lead AI Engineer

McKesson•Columbus, OH

10d

About The Position

The Lead AI Engineer provides technical leadership and architecture ownership for McKesson’s AI platform and AI-embedded delivery initiatives. As the senior technical authority on the AI Enablement team, this person sets architectural direction and standards for Senior and Associate AI Engineers, and represents AI platform architecture to engineering leadership and Legal, Compliance, and Privacy stakeholders. The role owns the technical design of agentic and LLM-powered systems built with large language model APIs, including retrieval-augmented generation (RAG), tool and connector integrations, and evaluation and governance tooling. It combines architecture leadership with hands-on delivery of high-complexity components in a regulated healthcare environment, with emphasis on reliability, security, scalability, privacy, and data provenance. The Lead AI Engineer also guides shared platform capabilities, establishes secure and observable LLM operations, evaluates model providers and vendors, and influences enterprise-wide AI platform decisions. Success is measured by the quality of systems shipped, adoption of the role’s architectural standards, and influence on platform direction.

Requirements

Bachelor’s degree or equivalent experience; typically 8 or more years of software engineering experience, including 3 or more years focused on AI/ML, LLM, or NLP systems in production. Fewer years are required with a relevant Master’s or Doctorate.
Demonstrated technical leadership above the Senior AI Engineer level, such as owning architecture for a team or platform area.
Deep, hands-on expertise building production LLM applications, including prompt engineering, RAG/GraphRAG, agentic workflows, tool use, and evaluation harnesses.
Experience with commercial large language model APIs, such as Anthropic or OpenAI, and tool-integration protocols such as Model Context Protocol (MCP).
Strong software engineering fundamentals in Python and/or Java, REST APIs, event-driven architecture, microservices, and cloud-native design.
Experience with vector or graph retrieval systems, embeddings, and knowledge-base architectures at production scale.
Working knowledge of LLMOps/MLOps, including usage attribution, cost/spend monitoring, abuse detection, and model evaluation pipelines.
Ability to set architectural standards and mentor engineers through design review and pairing.
Clear written and verbal communication for technical and executive audiences.
Must reside in the Columbus, Ohio metropolitan area for priority consideration; priority will be given to candidates in this area.
McKesson is unable to provide sponsorship for this role, presently or in the future.

Nice To Haves

Familiarity with regulated or healthcare data environments and AI governance frameworks, including model risk review, connector approval tiers, and PHI handling.
Advanced degree (M.S. or higher) in Computer Science, AI/ML, or a related technical field.
Experience leading AI/ML platform or applied-AI teams in healthcare IT or another highly regulated industry.
Experience modernizing legacy systems toward AI-embedded workflows, such as agent-assisted development, AI-powered risk scoring, or AI-driven automation.
Experience with Confluence/Jira-based engineering governance and enterprise architecture review boards.
Strong awareness of the evolving GenAI and agentic landscape and a demonstrated habit of continuous learning.
Experience managing relationships with AI tooling vendors and model providers.
Azure experience is preferred; AWS or GCP experience is acceptable.

Responsibilities

Define the technical architecture and roadmap for LLM and agentic systems, including model routing, connector and integration patterns, RAG pipelines, and prompt and evaluation frameworks.
Lead architecture reviews for new AI capabilities and ensure designs meet enterprise security, privacy, and data-provenance requirements.
Drive the technical design of shared AI platform capabilities, including knowledge-retrieval systems, citation and traceability of AI-generated outputs, and permission-aware data access.
Personally architect and build high-complexity components for priority AI delivery initiatives.
Set coding, architecture, and evaluation standards for Senior and Associate AI Engineers; run design reviews and pair on complex agentic workflows.
Establish secure, observable, and cost-attributed AI operations, including usage and spend attribution, abuse monitoring, model evaluation, and rollback strategy.
Partner with engineering managers, product, platform, and Legal and Compliance teams to align AI architecture decisions with delivery timelines and enterprise standards.
Evaluate foundation model providers and AI tooling vendors and make build-versus-buy and model-selection recommendations.
Ensure AI systems handling protected health information meet regulatory and enterprise governance requirements.
Represent AI architecture in cross-company forums and influence enterprise-wide AI platform direction.