Director, Applied AI

** _What the AI Center of Excellence contributes to Cardinal Health_*
* 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.

** Location**  - Open to candidates based nationwide (with willingness to travel quarterly into our global headquarters in Dublin, Ohio)

** Responsibilities*
* + Forward Deployed Use Case Delivery:
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.

+ Reusable Capabilities and Scaling:

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.

+ Stakeholder Partnership and Business Value:
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.

+ Team Leadership and Execution:
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.

+ High value AI use cases proven and scaled into production.

+ Measurable business value delivered through AI initiatives.

+ Reuse and adoption of accelerators, patterns, and reusable capabilities across teams.

+ Reduced duplication of AI effort across the enterprise.

+ Quality and growth of stakeholder partnerships and the funding pipeline.

+ Stronger alignment between AI strategy, governance, delivery, and business outcomes.

** Qualifications*
* + 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.

+ Technical Skills & Tech Stack:
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…