Head of AI Governance

Soni’s client is looking for a Head of AI Governance to lead the implementation and ongoing operation of the enterprise AI governance framework, ensuring governance requirements, standards, and controls are consistently embedded across AI solutions, including machine learning models, generative AI applications, copilots, intelligent agents, and retrieval-augmented generation (RAG) capabilities. This role is responsible for translating AI governance strategy into sustainable operational practices that support innovation, regulatory compliance, and effective risk management.

Working across Data Science, AI Engineering, Data Management, Data Governance, Risk, Legal, Compliance, Security, and Technology teams, the AI Governance Lead drives the integration of governance into day-to-day processes, platform capabilities, and delivery lifecycles to enable the responsible adoption of AI at scale.

Key Responsibilities

AI Governance Program Leadership

• Direct the execution of enterprise AI governance initiatives across analytical, generative AI, and autonomous agent use cases.
• Implement governance policies, standards, and control frameworks aligned with organizational risk tolerance, business objectives, and regulatory obligations.
• Establish scalable, repeatable governance practices that can be consistently applied across the organization.

Governance Operating Model & Process Integration

• Lead governance processes, including intake, assessment, approvals, escalation, monitoring, reporting, and change management.
• Oversee governance throughout the AI lifecycle, covering model development, deployment, monitoring, enhancement, and retirement.
• Govern AI-enabled solutions, including models, copilots, agents, and RAG-based systems, with consideration for retrieval methods, tool interactions, automated actions, and human oversight requirements.
• Embed governance controls into platforms, workflows, and delivery processes to ensure transparency, auditability, and long-term sustainability.

Risk, Controls & Compliance

• Operationalize standards governing the design, development, deployment, monitoring, and decommissioning of AI solutions.
• Implement risk-based review processes and controls based on business impact, use case complexity, data sensitivity, and regulatory considerations.
• Partner with Risk, Legal, Compliance, Privacy, and Security teams to ensure adherence to internal policies and external requirements.
• Monitor governance effectiveness, manage exceptions, and coordinate remediation activities when risks or control gaps are identified.

Cross-Functional Collaboration

• Act as a trusted advisor to business, product, engineering, analytics, and data science teams on AI governance requirements and best practices.
• Collaborate with Data Governance and Data Management leaders to align AI governance with enterprise data quality, stewardship, and control frameworks.
• Support governance councils and executive forums by providing risk insights, governance metrics, recommendations, and decision support materials.

Performance Monitoring & Continuous Improvement

• Define, track, and report governance performance metrics, including adoption, compliance coverage, risk events, control effectiveness, exceptions, and remediation outcomes.
• Evaluate governance processes, tooling, and operating practices to identify improvement opportunities and emerging risks.
• Continuously evolve governance capabilities to address new technologies, changing regulations, and advancing AI use cases.

Leadership & Stakeholder Influence

• Provide direction, coaching, and mentorship to governance team members, fostering expertise and operational excellence.
• Build alignment across business and technology functions through collaboration, influence, and pragmatic problem-solving.
• Engage senior leaders and key stakeholders to drive transparency, informed decision-making, and accountability for responsible AI practices.

Qualifications

• 12+ years of experience in data, analytics, artificial intelligence, technology governance, risk management, or related disciplines.
• Demonstrated success implementing and governing AI solutions, including machine learning models, generative AI applications, and agent-based systems within complex organizations.
• Strong understanding of AI lifecycle management, governance frameworks, responsible AI principles, data management, and risk controls.
• Experience desi…