McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care.

What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring current ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you.

Lead AI Engineer, Education Position Summary

The Lead AI Engineer, Education is a high-impact, enterprise-facing role within McKesson's Enterprise Analytics & AI Strategy organization. This role is equal parts AI evangelist, technical educator, and organizational change agent responsible for driving deep AI understanding and confident adoption across the full spectrum of McKesson's workforce, from C-suite executives to frontline operators. The role goes beyond awareness;

it builds genuinetechnical fluency in AI concepts, tools, and applications across business functions including Finance, HR, Supply Chain, Operations, Legal, and Technology, meeting each audience exactly where they are.

The Lead AI Engineer, Education serves as McKesson's most visible internal champion for AI, making the case for why AI matters, what it can do, and how every employee can leverage it to work smarter and deliver better outcomes. This role is a cornerstone of McKesson's AI strategy,



ensuring that enterprise-wide AI adoption is not just top-down mandated but bottom-up embraced powered by deep understanding, relevant skills, and an organization-wide culture of AI curiosity and innovation.

Key Responsibilities AI Evangelism & Culture Leadership

- Serve as McKesson's primary internal AI evangelist building excitement, urgency, and confidence around AI adoption across all levels of the organization, from board-level leadership to day-to-day operators.
- Design and lead enterprise-wide AI awareness and evangelism campaigns through McKesson's AI Hub, Viva Engage, internal newsletters, leadership forums, AI podcasts, and town halls making AI accessible, relevant, and compelling for every audience.
- Develop and deliver executive briefings and leadership narratives that connect AI capabilities directly to McKesson's strategic priorities, financial outcomes, and competitive positioning in clear, business-outcome language for C-suite and ELT audiences.
- Build and scale a McKesson AI Champions Network identifying, enabling, and sustaining AI ambassadors within every business function to drive grassroots AI enthusiasm and peer-driven adoption.
- Create compelling AI storytelling assets such as success story spotlights, use-case case studies, demo videos, infographics,



and impact narratives that translate real McKesson AI wins into inspiration for broader enterprise adoption.

Deep Technical AI Education — Audience-Adaptive Curriculum
- Design, build, and deliver a comprehensive, audience-adaptive AI education program calibrated by role, technical depth, and business context.

Audience‑Specific Curriculum Focus C‑Suite

Focus areas include AI strategy, competitive landscape, GenAI ROI, risk and governance, and decision‑making with AI.

Business Leaders

Curriculum emphasizes use‑case identification, AI ROI frameworks, responsible AI, and leading AI‑enabled teams.

Business Analysts / Finance / HR

Training covers Copilot productivity, prompt engineering, data‑driven decision‑making, and AI‑assisted workflows.

Data & Technology Teams

Deep technical modules on LLM architecture, RAG pipelines, MLOps, agentic AI, Azure AI Foundry, and fine‑tuning.

Frontline Operators

Focus on practical AI tool adoption (Copilot Chat, Copilot Agents), workflow automation, and foundational AI safety.

Deep Technical Curriculum Topics

- Large Language Models (LLMs): transformer architecture, tokenization, context windows, embeddings, attention mechanisms, pre‑training vs. fine‑tuning, and model evaluation.
- Generative AI: how GenAI models work, diffusion models, multimodal AI, image/text/code generation, and enterprise GenAI application patterns.
- Prompt Engineering: zero-shot, few-shot, chain-of-thought, structured prompting, system prompts, and role-based prompting with function‑specific use case labs.
- Retrieval-Augmented Generation (RAG): grounding LLMs in enterprise data, vector d

📌 Lead Ai Engineer, Education (Mississauga)
🏢 McKesson
📍 Mississauga