Principal AI Architect, Enterprise Platform & Strategy

McKesson Corporation; United States; On-site; USD 195,000 - 325,000; Full time

Job summary

McKesson Corporation is seeking a Principal AI Engineer to define the multi-year AI platform strategy and drive critical, high-risk AI initiatives across teams. You will serve as a senior IC, mentoring engineers and shaping governance around model risk, PHI handling, and tool ecosystems.

The role requires deep hands-on experience with production LLM applications, including RAG, prompt engineering, and evaluation pipelines, plus strong cloud and security fundamentals.

Qualifications

• Bachelor's degree or equivalent; typically 12+ years of software engineering, including 6+ years focused on AI/ML in production and 3+ years as a senior IC.
• One level of demonstrated technical leadership above a Lead AI Engineer, setting direction across multiple teams.
• Proven track record of strategy adopted by multiple teams and durable bets in GenAI/LLM environments.

Responsibilities

• Own the multi-year AI platform strategy and roadmap across all teams building on it.
• Serve as the final technical escalation point for architecture and reliability problems.
• Set architecture, security, and evaluation standards adopted by multiple teams.
• Advise VP and executive leadership on AI platform strategy and model risk.
• Lead architecture and prototyping for high-ambiguity AI initiatives.
• Mentor Lead and Senior AI Engineers through design reviews and coaching.
• Shape enterprise-wide AI governance and PHI-handling standards.

Skills

AI/ML strategy

LLM systems

Python

Java

REST APIs

Cloud-native design

MLOps

Leadership

Education

MS/PhD in CS/AI/ML

Tools

Azure

OpenAI API

Model Context Protocol (MCP)

Vector databases

Job description

McKesson Corporation is seeking a Principal AI Engineer to define the multi-year AI platform strategy and drive critical, high-risk AI initiatives across teams. You will serve as a senior IC, mentoring engineers and shaping governance around model risk, PHI handling, and tool ecosystems.

The role requires deep hands-on experience with production LLM applications, including RAG, prompt engineering, and evaluation pipelines, plus strong cloud and security fundamentals.