Director, People AI Engineering & Innovation

AI Strategy and Technical Roadmap: Define and evolve the People organization’s AI strategy, architecture, platform direction, and capability roadmap, translating business priorities and emerging technologies into actionable plans.

Engineering Standards and Delivery Framework: Establish reusable technical patterns, engineering standards, decision frameworks, and delivery practices to move AI solutions from concept to production, stabilization, and formal ownership transfer.

Opportunity Assessment and Solution Selection: Partner with executive sponsors, People teams, and the Execution COE to assess business opportunities, determine technical feasibility, and evaluate whether AI, Workday, automation, process simplification, or another platform is the most appropriate solution.

Hands-On AI Engineering: Serve as the technical authority for People AI solutions across approved platforms, participating directly in discovery, prototyping, architecture design, workflow and integration design, technical reviews, evaluations, and troubleshooting.

End-to-End Product Delivery: Lead approved AI solutions through discovery, design, development, validation, launch, and stabilization. Establish grounded-solution approaches, evaluation methods, documentation, and reusable engineering practices.

Security, Privacy, and Responsible AI: Integrate security, privacy, identity, access controls, least-privilege permissions, and People-data protection throughout the AI product lifecycle. Partner with IT, Infrastructure, Information Security, Privacy, Legal, and enterprise AI governance teams.

Governance and Risk Management: Ensure appropriate human oversight, escalation procedures, monitoring, auditability, and controls for sensitive or higher-risk use cases. Retain authority over architecture, security, access, integrations, model changes, and production readiness, and pause changes that do not meet required standards.

Value Measurement and Lifecycle Management: Define success metrics covering adoption, quality, capacity released, user experience, control effectiveness, and return on investment. Partner with product owners and the Execution COE to review results and recommend whether solutions should scale, improve, remain unchanged, or be retired.

Ownership and Technical Governance: Complete formal transfers to designated business or product owners following stabilization, while maintaining technical governance over architecture, security, integrations, model and platform changes, evaluations, monitoring, and production readiness.

Cross-Functional Operating Model: Establish clear operating interfaces, decision rights, design authorities, service expectations, escalation paths, and lifecycle responsibilities across AI Innovation, Workday and People Technology, People Data and Analytics, experience platforms, functional teams, the Execution COE, and enterprise partners.

Workday and Enterprise Collaboration: Coordinate with Workday leadership when solutions involve Workday data, processes, roles, integrations, or transactions, ensuring Workday remains the authoritative system of record.

Organizational Education and Innovation: Support discovery, intake, prioritization, portfolio governance, demonstrations, and reusable examples that help business leaders understand AI capabilities and opportunities.

Team Leadership and Talent Development: Build and lead a high-performing AI product and engineering team, setting clear expectations and accountability. Coach team members, support differentiated development and stretch assignments, build succession capability, and hire for skill gaps across architecture, engineering, security, evaluation, responsible AI, and production delivery.

External Partner Management: Use external partners selectively while retaining internal technical authority, knowledge, accountability, and ownership.