below marketmost similar roles pay hereabove market
This role pays more than
65%
of similar roles. Most pay
$154,600–$230,250
— the blue band above.
At the midpoint, this role pays about
$207k
versus about
$192k
for comparable roles.
Based on 240 similar postings.
Employer
About McKesson Corporation
McKesson Corporation is a premier American healthcare services company that distributes pharmaceuticals, medical-surgical supplies, and provides technology to the healthcare industry.
McKesson Corporation currently has
143 open roles
on FindRole.
Listed pay typically runs
$124,300–$207,200
across 78 roles with salary data.
JOB TITLE: Lead AI Engineer
The Lead AI Engineer joins the AI Enablement team to serve as the senior technical authority for the company's AI platform and embedded delivery initiatives. This role involves providing technical leadership, setting architectural standards, and mentoring other engineers. Key responsibilities include designing agentic and LLM-powered systems, managing RAG pipelines, and establishing LLMOps for production systems. The successful candidate will build high-complexity components, oversee model routing, and ensure designs meet security and privacy requirements in a regulated healthcare environment. Required skills include deep expertise in prompt engineering, tool use, and evaluation harnesses using commercial APIs like Anthropic and OpenAI. Proficiency in Python or Java, REST APIs, microservices, and cloud-native design is essential for managing vector retrieval systems and knowledge-base architectures at production scale.
Define the technical architecture and roadmap for LLM and agentic systems.
Lead architecture reviews to ensure designs meet security, privacy, and data-provenance requirements.
Drive the technical design of shared platform capabilities like knowledge-retrieval and traceability.
Build high-complexity components for priority AI delivery initiatives as working systems.
Set coding and architecture standards while mentoring engineers through design reviews and pairing.
Establish secure LLMOps for usage attribution, abuse monitoring, and model evaluation.
Evaluate foundation model providers and make build-vs-buy and model-selection recommendations.
Ensure AI systems handling protected health information meet regulatory and enterprise governance requirements.
What we're looking for
Bachelor's degree or equivalent experience; typically 8+ years of software engineering experience, including 3+ years focused on AI/ML, LLM, or NLP systems in production.
Fewer years of experience required with a relevant Master's or Doctorate.
Demonstrated technical leadership one level above a Senior AI Engineer, 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 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).
Experience with vector/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.
Familiarity with regulated/healthcare data environments and AI governance frameworks is a plus.
Advanced degree (M.S. or higher) in Computer Science, AI/ML, or a related technical field (preferred).
Prior experience leading AI/ML platform or applied-AI teams inside healthcare IT or another highly regulated industry (preferred).
Experience modernizing legacy systems toward AI-embedded workflows (preferred).
Experience with Confluence/Jira-based engineering governance and enterprise architecture review boards (preferred).
Python
Java
Large Language Models
Generative AI
MLOps
RAG
Vector Databases
AWS
GCP
Azure
Microservices
APIs
Data Pipelines
Distributed Systems
Agile
OCR
Semantic Search
Embeddings