Lead AI Engineer
About The Position
The Lead AI Engineer provides technical leadership and architecture ownership for McKesson’s AI platform and embedded AI delivery initiatives. As the senior technical authority on the AI Enablement team, this role sets the architectural direction and standards that Senior and Associate AI Engineers follow, 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 on large language model APIs, including retrieval-augmented generation (RAG), tool and connector integration, and evaluation and governance tooling in a highly regulated healthcare environment. It combines architecture leadership with hands-on work building complex components for priority initiatives. Success is measured by the reliability, security, and scalability of deployed AI systems, engineers’ adoption of architectural standards, and influence on organization-wide AI platform decisions.
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 experience building production LLM applications, including prompt engineering, RAG or GraphRAG, 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).
- Strong software engineering fundamentals in Python and/or Java, REST APIs, event-driven architecture, microservices, and cloud-native design; Azure preferred, with AWS or GCP acceptable.
- Experience with vector or graph retrieval systems, embeddings, and production-scale knowledge-base architectures.
- Working knowledge of LLMOps/MLOps, including usage attribution, cost and spend monitoring, abuse detection, and model-evaluation pipelines.
- Ability to set architectural standards and mentor engineers through design review and pairing.
- No employment sponsorship is available, 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.
- Prior 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 fast-moving GenAI and 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 technical and executive audiences.
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 technical design of shared AI platform capabilities, including knowledge retrieval, 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; conduct 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 strategies.
- Partner with engineering managers, product, platform, and Legal and Compliance teams to align 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 that handle protected health information meet regulatory and enterprise governance requirements.
- Represent AI architecture in cross-company forums and influence enterprise-wide AI platform direction.
Benefits
- Competitive compensation package
- Annual bonus or long-term incentive opportunities may be offered
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
