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CoverMyMeds is recognized across McKesson as a leader in AI adoption. Our Technology organization runs on an AI-First operating model, with AI embedded in how we plan, build, secure, and deliver software. We are looking for a Senior Director, AI Enablement to lead the next stage of this transformation.
This Sr Director, AI Enablement will own AI strategy, governance, and AI engineering platforms for CoverMyMeds, and will build and deliver AI products using an agentic, AI-native approach, where AI agents are designed into products and workflows from the start rather than added later. The role scales safe and measurable AI adoption across engineering and business teams, and partners with McKesson\'s Enterprise AI team so capabilities are built once and reused across business units. This is a builder\'s role for someone who combines technical depth, product delivery, strategic vision, and the ability to change how people work.
Key Responsibilities:
AI Strategy and Roadmap
- Define and own the AI enablement strategy and multi-year roadmap for CoverMyMeds, aligned with business priorities and McKesson\'s enterprise AI direction.
- Manage a portfolio of concurrent AI initiatives and a multimillion-dollar budget, prioritizing investments by business value, risk, and reuse.
- Measure and report AI adoption, productivity, and ROI to executive leadership.
Agentic-Native Product Build and Delivery
- Own the build and delivery of AI products and agents end to end, from discovery and design through production release and ongoing improvement.
- Champion an agentic-native approach: design products and workflows around AI agents from the start, with humans in the loop where it matters.
- Partner with Product and business leaders to identify high-value use cases and turn them into products that deliver measurable clinical, operational, and financial outcomes.
- Use AI-accelerated delivery to move from idea to production in days and weeks, while holding the bar on quality, security, and compliance.
Governance and Responsible AI
- Chair the AI Review Board and own AI usage policy, model-selection frameworks, and token allocation and cost management.
- Ensure HIPAA, privacy, and security requirements are built into AI development and delivery processes.
- Partner with Legal, Privacy, Security, and Compliance to enable fast, safe, and responsible AI adoption.
AI Platforms and Engineering Acceleration
- Lead the teams that build and run reusable AI capabilities, including the AI Skills Marketplace, MCP servers, agents, and developer tooling.
- Advance the AI-embedded SDLC, including spec-driven development, AI code review, automated vulnerability remediation, shift-left testing, and self-service developer onboarding.
- Drive build-once, reuse-everywhere practices so shared AI assets serve multiple teams and business units.
Adoption, Enablement, and Change
- Scale AI adoption beyond engineering to product, business, legal, finance, privacy, call center, and platform teams.
- Own the AI training curriculum and champions network, from foundational AI literacy to advanced agentic workflows.
- Advise senior leadership and the C-suite on AI strategy, capabilities, and risk.
- Build a positive, curious AI culture through townhalls, ERG sessions, and hands-on enablement.
Partnership and Leadership
- Partner with McKesson\'s Enterprise AI team, architecture, product, and platform leaders to align standards and avoid duplicate effort.
- Work with external technology partners and vendors to evaluate and adopt emerging AI capabilities.
- Hire, develop, and retain a high-performing team of engineers, architects, and enablement specialists.
Basic Qualifications:
- Bachelor\'s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent experience.
- Typically requires 13+ years of relative software engineering and technology leadership experience, including 5+ years leading engineering or platform teams.
- 3+ years of hands-on leadership in AI or ML, including generative AI, LLMs, and agentic AI in production environments.
- Proven track record of driving AI adoption at scale with measurable outcomes in productivity, quality, or delivery speed.
- Demonstrated experience building and delivering AI-powered products or agents to production, with measurable business impact.
- Experience establishing AI governance, responsible AI practices, or technology standards in a regulated environment.
- Experience managing people, budgets, vendor relationships, and a portfolio of concurrent initiatives.
Preferred Experience:
- Master\'s degree in a technical field or an MBA.
- Healthcare, pharmacy, or health technology experience, including HIPAA and patient data protection.
- Experience with AI developer tooling such as Claude, Claude Code, GitHub Copilot, and Model Context Protocol (MCP).
- Experience with FHIR and healthcare interoperability standards.
- Experience in a matrixed, multi-business-unit enterprise.
Technical Skills:
- Generative and agentic AI: LLMs, RAG, agents, prompt and context engineering, model evaluation.
- Agentic-native product design: multi-agent workflows, tool use, human-in-the-loop design, and agent observability and evaluation.
- Product delivery: discovery, MVP definition, rapid iteration, and production release.
- AI platform engineering: MCP, reusable skills and plugins, AI marketplaces, LLMOps.
- Modern SDLC: spec-driven development, CI/CD, DevOps, Cyber Security, automated testing and code review.
- Cloud-native architecture (Google Cloud Platform or AWS), APIs, and integration platforms.
- Data foundations for AI, including data quality, tagging, and synthetic data.
Leadership:
- Strategic vision and the ability to turn it into an executable roadmap.
- Executive communication and influence, including explaining complex technical topics simply.
- Change leadership: moving teams from skepticism to confident, everyday AI use.
- Cross-functional partnership with Legal, Privacy, Security, Product, and business leaders.
- Talent development and building inclusive, high-performing teams.
- Data-driven decisions, with a focus on measurable outcomes and ROI.
What Success Looks Like in the First Year:
- AI adoption continues to grow across engineering and expands further into business functions.
- New agentic-native AI products are delivered to production with measurable business outcomes.
- Governance keeps pace with new AI capabilities without slowing delivery.
- Reusable AI assets are adopted across multiple teams and business units, reducing duplicate effort.
- Clear, regular reporting on AI adoption, productivity, and ROI to executive leadership.
We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson\'s pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please
Our Base Pay Range for this position
$174,800 - $291,300
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