Director, Applied AI

Cardinal Health

2d•$135,400 - $208,100•Remote

About The Position

The AI Center of Excellence (CoE) advances healthcare by applying data science and artificial intelligence to the enterprise's highest value problems. As the central hub for artificial intelligence at Cardinal Health, the CoE drives Enterprise AI Strategy, AI Enablement, AI Governance, and Enterprise AI Products in close partnership with technology and business leaders across the company. Through these pillars, we collaborate to build and scale reusable patterns, internal accelerators, and responsible AI practices, turning data into solutions that improve patient and customer outcomes, streamline operations, reduce cost, and strengthen the overall healthcare experience. Reporting to the Head of the AI Center of Excellence, this role leads our Applied AI engineering efforts. While our AI Product leaders own the long term enterprise capabilities, this role acts as the "builder and execution engine" for specific business use cases. The operating model is deliberately structured as an internal, forward deployed capability: your team will partner directly with various business and IT stakeholders across the company to prove use cases through rapid proofs of concept, move the strongest into production, transition matured solutions to delivery or run teams, and immediately redeploy to the next high value internal opportunity. This keeps the team continuously focused on what is unproven and most valuable next, turning field learnings into reusable enterprise capabilities.

Requirements

Ideally targeting individuals with 10-12+ years of progressive experience in technology, data, AI, or product delivery, with 5+ years leading AI, machine learning, data science, analytics, or AI enabled engineering teams.
Strong working knowledge of generative AI, machine learning, data platforms, and modern software delivery.
Familiarity with the Google Cloud ecosystem (GCP, Vertex AI, Gemini suite of products) and Python is highly desirable.
Player coach depth, able to shape solution architecture, prototype and build proofs of concept, and review engineering work without requiring full time individual contribution.
Familiarity with LLMs, agentic AI, and AI product delivery is a plus.
Highly flexible and able to span the enterprise. Your team could deploy into any internal business unit, so this leader must learn quickly, balance priorities across all businesses, and know enough to effectively guide the work.
Exceptional solutioning skill set. Stakeholders will come to this team for help solving complex problems that their teams may not be able to do themselves, so having the ability to help offer up creative solutions is important.
Demonstrated ability to engage business and IT leaders, understand their needs, shape use cases, and deliver tailored solutions that produce measurable outcomes.
Experience building business cases and supporting funding and prioritization decisions.
Excellent written and verbal communication, facilitation, and executive presentation skills, with the ability to translate complex AI concepts into practical business and delivery guidance.
Demonstrated ability to lead and develop technical teams, direct senior individual contributors, and coordinate matrixed resources across offshore teams, contractors, and vendors on multiple high priority initiatives.
Strong product mindset, connecting stakeholder needs to reusable enterprise capabilities.
Experience operating in an embedded, internal forward deployed, or field technology leadership model.

Nice To Haves

Experience with AI Centers of Excellence, enterprise AI platforms, reusable AI patterns, internal accelerators, and AI product adoption a plus.
Track record of scaling AI initiatives from proof of concept into production and broad adoption.

Responsibilities

Partner with internal business and IT stakeholders to identify, shape, and prioritize high value AI use cases based on value, feasibility, and risk.
Lead the delivery of those use cases as a player coach, shaping architecture, building or co-building proofs of concept, and reviewing the team's engineering work from idea through production.
Move the strongest proofs of concept into scalable, supportable, production ready solutions rather than leaving them as isolated pilots.
Build and promote reusable patterns, internal accelerators, and solution assets that scale across multiple business areas, reducing duplication and accelerating delivery.
Transition matured solutions to delivery or run teams at the build to run handoff, and redeploy the team to the next high value opportunity.
Feed common needs and field learnings into the AI CoE product, engineering, and enablement roadmaps.
Define the business case for AI initiatives: expected value, investment, delivery approach, adoption plan, and success metrics, and support funding and prioritization discussions.
Secure funding for high value AI initiatives by pitching business cases directly to executive sponsors.
Connect every initiative to measurable outcomes: productivity, cost reduction, working capital, revenue, risk reduction, service quality, and patient or customer experience.
Establish value tracking discipline, including baseline metrics, adoption measures, and business impact.
Lead and develop AI engineers and AI engineering managers; set technical direction and standards for the team.
Coordinate matrixed delivery across offshore engineering teams, contractors, vendors, and AI CoE partners; remove barriers, clarify ownership, and maintain momentum.
Support executive updates, roadmap reviews, and decision making materials related to applied AI delivery and scaling.
Ensure initiatives follow responsible AI, data protection, security, privacy, compliance, and governance expectations.
Partner with AI governance and risk teams to navigate review processes without slowing practical execution.
Champion scalable, secure, supportable solutions that can be reused across internal teams and business areas.