Key Skills

Job Description

• Drive implementation of AI governance processes across the end-to-end AI lifecycle (data, development, validation, deployment, monitoring, and retirement) • Embed governance controls into AI/ML platforms, MLOps pipelines, and AI lifecycle management • Own AI governance operations including AI Registry, model/agent inventory, AI asset tiering, approvals, at testations • Establish governance automation to improve compliance, reduce manual effort, and avoid policy based governance that is disconnected from delivery • Ensure traceability, auditability, and transparency of AI assets, models, and decisions • Develop governance metrics, dashboards, and KPIs to measure compliance, adoption, risk, and effectiveness • Partner with Data & AI, IDSO, Enterprise Architecture, IT Engineering, and CISO teams to integrate governance into enterprise platforms • Support regulatory, audit, and assurance activities through robust evidence management and reporting • Drive continuous improvement of governance processes, tooling, and operating models. Experience • 8-12+ years in AI/ML, technology operations, governance, risk, or digital transformation. • Experience implementing enterprise governance processes and operating models. • Experience with AI/ML platforms, MLOps, DevSecOps, or data platforms. • Proven track record of operationalizing controls and driving enterprise adoption. Expertise • AI lifecycle management and MLOps • Governance process design and automation • AI risk, controls, and compliance • Data and model management platforms • Metrics, reporting, and operational excellence • Process optimization and continuous improvement Behaviors • Systems thinker with a strong operational mindset • Balances governance rigor with business agility • Drives simplification and automation • Influences across organizational boundaries • Focuses on outcomes, scalability, and adoption

Core Responsibilities

The Data Engineer will drive the implementation of AI governance processes across the entire AI lifecycle, including development, deployment, and monitoring. They will also establish automation for compliance and manage AI registries, model inventories, and asset tiering.

Requirements

Candidates must have 8-12+ years of experience in AI/ML, technology operations, or governance with a proven track record of operationalizing controls. Expertise in MLOps, AI risk management, and enterprise governance process design is essential for this role.