Head of AI Governance

Role Purpose 

Lead the execution of Guardian’s AI governance program by operationalizing enterprise policies, standards, and guardrails across AI models, copilots, agents, and RAG‑based workflows. This role ensures AI governance is consistently implemented, scalable, and embedded into delivery and platform workflows—enabling responsible AI adoption while managing risk. 

The AI Governance Lead partners closely with Data Science, AI Platforms, Data Management, Data Governance, Risk, Legal, and Technology teams to translate enterprise AI governance strategy into practical processes, controls, and day‑to‑day ways of working. 

Responsibilities 

AI Governance Execution & Program Leadership 

• Lead the execution of Guardian’s AI governance program across analytics, AI, and agentic use cases. 

• Implement enterprise AI governance policies, standards, and guardrails in alignment with defined risk appetite and regulatory expectations. 

• Ensure governance processes are repeatable, risk‑based, and scalable—not bespoke or ad hoc. 

Operating Model & Workflow Integration 

• Own the AI governance operating model, including intake, review, escalation, monitoring, and change management processes. 

• Govern AI model lifecycles and AI system behaviors across models, copilots, agents, and RAG workflows, including retrieval strategies, tool use, actions, and human‑in‑the‑loop requirements. 

• Embed AI governance controls into platforms and delivery workflows so controls are observable, durable, and auditable. 

Standards, Controls & Risk Management 

• Operationalize governance standards for AI development, deployment, monitoring, and retirement. 

• Implement risk‑based controls and escalation paths based on use case type, data sensitivity, and impact. 

• Partner with Risk, Legal, Compliance, and Security teams to ensure AI governance execution meets internal and external requirements. 

Cross‑Functional Partnership 

• Serve as the primary AI governance partner to data science, analytics, engineering, and product teams. 

• Coordinate with Data Management and Data Governance leaders to ensure AI governance aligns with data controls, quality expectations, and AI‑readiness requirements. 

• Support enterprise governance forums by preparing materials, surfacing risks, and enabling timely decision‑making. 

Measurement & Continuous Improvement 

• Define and track operational KPIs for AI governance execution, including coverage, exceptions, monitoring effectiveness, and remediation trends. 

• Identify gaps,