Establishes responsible AI principles, policy compliance, ethical risk assessment, and cross-functional accountability for AI systems.
Ensures data is collected, curated, versioned, and governed as a first-class engineering asset underpinning all AI work.
Applies rigorous practices to model development, feature engineering, experiment tracking, and reproducible training.
Manages the safe, reliable deployment, monitoring, and continuous operation of AI systems in production environments.
Tests AI for accuracy, fairness, robustness, and alignment to intended purpose before and after deployment.
Validates AI use cases against real user needs and business problems before investing in full-scale model development.
Uses outcome data to continuously evaluate, challenge, and evolve AI systems and team practices.
Builds cross-functional AI teams with clear roles, shared standards, psychological safety, and sustainable ways of working.