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 genuine technical 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 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. 

• Develop deep technical curriculum covering the following AI/ML and LLM concepts taught accessibly to non-technical audiences and rigorously to technical ones:  

• 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 databases, chunking strategies, embedding models, and RAG pipeline architecture. 

• Agentic AI & Multi-Agent Systems: what agents are, how they reason and act, orchestration patterns, tool use, Copilot Agents, and when to deploy agents vs. standard AI tools. 

• Machine Learning Fundamentals: supervised/unsupervised learning, model training, overfitting, evaluation metrics, and ML lifecycle management. 

• Responsible AI & AI Governance: hallucination risks, bias and fairness, data privacy in AI usage, McKesson's Acceptable Use Policy, AIRB approval process, and ethical AI principles. 

• AI in Business Functions: function-specific deep dives Finance (forecasting, variance analysis, contract summarization), HR (talent analytics, policy agents, JD drafting), Supply Chain (demand forecasting, anomaly detection), Operations (workflow automation, predictive maintenance). 

McKesson AI Tool Stack — Hands-On Enablement 

Design, facilitate, and scale hands-on training programs across McKesson's full enterprise AI toolkit: 

Productivity & Collaboration AI 

• Microsoft 365 Copilot — Copilot in Teams, Outlook, Word, Excel, PowerPoint; Intelligent Recap, email summarization, document drafting, and meeting intelligence. 

• Microsoft Copilot Chat — Secure, enterprise-grade AI chat for all McKesson employees for Q&A, content generation, and workflow support. 

• Copilot Agents — No-code intelligent agents for automating repetitive tasks, decision support, and business workflow automation. 

• GitHub Copilot — AI-accelerated coding, documentation, code review, and issue resolution for technical teams. 

Generative AI & Development Platforms 

• Azure OpenAI Service — Enterprise LLM APIs (GPT-4o, GPT-4 Turbo, o1, o3) for custom GenAI applications, RAG pipelines, and intelligent agent development. 

• Anthropic Claude (Claude Models & Claude Code) — Advanced frontier LLM capabilities including Claude 3.5/3.7 Sonnet, Claude Opus, and Claude Code for agentic coding, long-context reasoning, document analysis, and complex multi-step task automation; a key complement to OpenAI models in McKesson's enterprise GenAI stack. 

• Azure AI Foundry — Unified platform for building, evaluating, and deploying enterprise-grade AI models and GenAI applications across multiple model providers including OpenAI and Anthropic. 

• Azure Databricks — Unified data and AI platform supporting ML model development, LLMOps, GenAI app development, RAG pipelines, and fine-tuning at enterprise scale. 

• Azure Machine Learning (Azure ML) — Enterprise MLOps for model training, deployment, governance, and monitoring. 

• LangChain / Agentic AI Frameworks — Orchestration frameworks for multi-agent AI systems and complex GenAI workflows. 

• Codeium / Windsurf — AI-powered code completion and developer productivity for advanced engineering teams. 

• Exafunction — GPU compute optimization for AI model training and inference workloads. 

Governance & Program Tools 

• AI Hub (SharePoint) — McKesson's central AI resource hub for governance, learning, tool access, use-case submission, and community connection. 

• Responsible AI Use Case Intake (AIRB / Smartsheet) — AI Review Board submission and approval workflow for enterprise AI use cases. 

• Workday Learning / Viva Learning / MyLearning / LinkedIn Learning — Enterprise learning platforms for deploying and tracking AI curricula at scale. 

Cross-Functional Stakeholder Engagement 

• Serve as the primary liaison between the Enterprise AI team and all business functions translating AI capabilities into audience-appropriate learning and enablement experiences. 

• Collaborate with HR/L&D, Change Management, and Internal Communications to embed AI education into enterprise talent development programs and communications channels. 

• Partner with AI engineers, data scientists, and product teams to keep all curricula current with tools, platforms, and use cases in active production. 

• Represent AI Education priorities in governance forums, BU pod meetings, and Enterprise AI Steering Committees. 

Program Management & Impact Measurement 

• Own the AI Education & Evangelism roadmap — define milestones, track adoption KPIs (MAU, WAU, training completions, use cases enabled), and report outcomes to senior leadership. 

• Maintain the AI Hub Education section as the single source of truth for all AI learning content, resources, and governance updates. 

• Build assessments, feedback loops, and adoption scorecards to measure the effectiveness of learning programs and AI fluency progress across the enterprise. 

• Identify and manage external training vendors and content partners (e.g., Microsoft, Anthropic, LinkedIn Learning, Databricks Academy) as needed to supplement internal curriculum. 

Minimum Job Qualifications 

Required Skills & Experience 

AI/ML & LLM Technical Depth 

• Solid understanding of Large Language Models (LLMs) — including transformer architecture, tokenization, context windows, embeddings, pre-training vs. fine-tuning, prompt engineering (zero-shot, few-shot, chain-of-thought), and RAG pipeline fundamentals. 

• Working knowledge of Generative AI concepts — GenAI application patterns, agentic AI, responsible AI principles, hallucination risks, and AI governance frameworks. 

• Hands-on proficiency with at least one frontier LLM platform — Azure OpenAI (GPT-4o, o1/o3 series) or Anthropic Claude (Claude 3.5/3.7 Sonnet, Claude Opus, Claude Code) for building or enabling enterprise AI applications. 

• Familiarity with Microsoft 365 Copilot ecosystem (Copilot Chat, Copilot Agents, M365 Copilot) and enterprise AI productivity tools. 

AI Evangelism & Education 

• Demonstrated AI evangelism and storytelling skills — proven ability to build organizational excitement and confidence around AI adoption across all levels, from C-suite to frontline operators. 

• Curriculum design and instructional delivery expertise — ability to build audience-adaptive, modular learning programs from scratch using adult learning principles (ADDIE, SAM, or equivalent). 

• Executive communication and facilitation skills — ability to command the room with C-suite and ELT audiences as comfortably as with frontline teams; translate technical AI concepts into compelling business narratives. 

Preferred Skills & Experience (Nice to Have) 

Advanced AI Tool Proficiency 

• Hands-on experience with both Azure OpenAI and Anthropic Claude (Claude Code, Claude Opus) — ability to compare, contextualize, and teach across multiple frontier model providers. 

• Proficiency with Azure Databricks, Azure ML, and MLflow for ML model development, LLMOps, and GenAI pipeline management. 

• Experience with LangChain or similar agentic AI orchestration frameworks for multi-agent system design and deployment. 

• Familiarity with GitHub Copilot, Codeium/Windsurf for developer-focused AI productivity enablement. 

• Proficiency with Power BI / Copilot Analyst and Power Automate for enabling non-technical business teams on AI-powered analytics and workflow automation. 

• Familiarity with Snowflake, Azure AI Foundry, or Exafunction for enterprise data and AI infrastructure. 

Deep Technical Education 

• Experience designing deep technical AI curricula for engineering and data science audiences — covering LLM architecture, RAG pipeline design, MLOps, fine-tuning, and agentic AI system patterns. 

• Familiarity with McKesson's Responsible AI principles, Acceptable Use Policy, and AIRB governance process. 

Industry & Enterprise Context 

• Prior experience in healthcare, pharmaceutical distribution, or a regulated industry. 

• Familiarity with McKesson's AIM28 program, enterprise AI ecosystem, and BU structure. 

• Experience building and scaling an AI Champions Network or equivalent grassroots adoption program in a large, matrixed organization. 

Certifications (Any of the following) 

• Microsoft Azure AI Engineer (AI-102) or Azure Data Scientist (DP-100) 

• Microsoft 365 Copilot Adoption Specialist 

• Databricks Generative AI Fundamentals 

• Anthropic Claude Certification (if available) 

• ATD Certified Professional in Talent Development (CPTD) 

Education 

• Master's degree in Computer Science, AI, Data Science, Instructional Design, or a related field. 

• 10+ years of experience with at least 3 years in a lead or senior IC capacity.