Role: AI Governance Lead – Practitioner
Location: Toronto, ON (Onsite)
Role Summary
We are seeking an experienced AI Governance Lead (Practitioner) to lead and operationalize AI governance and Responsible AI practices across enterprise AI, Generative AI, and agentic AI solutions.
The role establishes practical governance mechanisms for AI use-case intake, inventory and classification, risk assessment, Responsible AI, model risk management, data/privacy, security, regulatory readiness, human oversight, deployment approval, and ongoing monitoring.
The practitioner collaborates with business, AI engineering, architecture, cybersecurity, data governance, privacy, legal, compliance, and risk teams to ensure AI solutions are developed and operated responsibly throughout their lifecycle.
Key Responsibilities
AI Governance Framework & Operating Model
- Implement and operationalize an enterprise AI Governance and Responsible AI framework.
- Define governance policies, standards, controls, decision rights, and approval processes.
- Establish clear accountability across business, engineering, data, security, privacy, legal, and risk functions.
- Embed governance checkpoints into AI development and deployment processes.
AI Inventory, Classification & Risk Assessment
- Maintain an inventory of enterprise AI systems and use cases, and ensure accountable ownership for each.
- Classify AI solutions by capability, business use, data sensitivity, and potential impact; establish risk-based governance requirements.
- Maintain governance documentation and evidence.
Model Risk Management & AI Approval
- Own model risk management practices for AI/GenAI models, including validation expectations and ongoing performance oversight.
- Own AI approval processes, including governance gates and production release approvals.
Responsible AI, Lifecycle & GenAI / Agentic AI Governance
- Embed Responsible AI principles (fairness, transparency, explainability, safety, privacy, human oversight) into the AI lifecycle.
- Define governance controls specific to GenAI and agentic AI, including prompt-injection risk, tool/API access, guardrails, model evaluation, and model/data lineage.
Regulatory Readiness, Third-Party AI & Assurance
- Own regulatory readiness for AI, mapping controls to applicable regulations and frameworks.
- Own third-party / vendor AI governance, including assessment of externally sourced AI solutions.
- Lead governance assurance activities, including control testing, audit readiness, and support for internal and external audits.
Risk, Security, Privacy & Production Monitoring
- Partner with security, privacy, and risk teams on AI risk, security, and privacy controls.
- Establish continuous governance and monitoring for AI solutions in production.
Experience & Qualifications
- Hands-on experience implementing an AI Governance or Responsible AI program.
- Experience governing enterprise GenAI/LLM solutions.
- Experience working with AI engineers, data scientists, and architects.
- Experience conducting AI risk/control assessments and establishing AI inventories and risk classifications.
- Experience supporting governance reviews and production approvals.
- Experience with audits, regulatory compliance, and governance programs in regulated industries such as insurance or financial services.
- Experience working with security, privacy, compliance, risk, and legal teams.
- Experience assessing third-party/vendor AI solutions.
Key Attributes
- Stakeholder management and cross-functional leadership.
- Critical thinking and risk-based decision making.
- Communication and executive presentation; facilitation skills.
- Ability to constructively challenge technical teams and translate technical AI risks into business language.
- Strong documentation skills and attention to detail.
Must-Have Skills
- AI Governance and Responsible AI
- AI risk management and model risk management
- AI lifecycle governance
- AI use-case classification and inventory/registry management
- Governance controls and evidence
- Human-in-the-loop governance
- AI monitoring and oversight
- Third-party/vendor AI governance
- Technical depth sufficient to challenge engineering teams and assess controls around: Generative AI / LLMs, RAG, AI agents, foundation models, prompt-injection risks, APIs and tool integrations, model/data lineage, guardrails, model evaluation, AI security, and logging/monitoring (does not need to be an AI developer)
