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AI Governance Specialist
Job at a glance
- Category
- AI Governance
- Work arrangement
- On-site
- Location
- Toronto, Canada
- Salary range
- $95,000 to $135,000
- Posted
- Aug 31, 2026
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Mobility is hiring a AI Governance Specialist in Toronto, Canada. This is a AI Governance job in the governance, risk, and compliance field, with a posted range of $95,000 to $135,000. Review the full details below and apply directly with Mobility.
Role Summary: Reporting to the Director, AI Architecture & Governance , this role executes and maintains the AI Standards and Processes , governance tooling, AI inventory, and compliance evidence that operationalize the team's AI governance framework. It provides the day-to-day, hands-on capacity behind the governance and compliance function. Responsibilities: Develops and maintains the AI Standards and Processes covering acceptable use, model risk management, third-party AI vendor assessment, and AI data governance Operationalizes AI data governance controls for AI-specific concerns: training data provenance and lineage, PII handling in training and inference pipelines, data bias and representativeness requirements, consent management, and right-to-erasure implications for trained models Tracks the evolving regulatory landscape and drafts updates to the AI standards and processes for review Maintains the AI inventory and model registry; audits entries for completeness and accuracy while project teams register and maintain their own Assembles and maintains compliance evidence (audit trails, control attestations, regulatory mappings) demonstrating adherence to AI regulations and internal standards Builds and operates governance tooling and automation (dashboards, audit trails, compliance checks) Background & Qualifications: Experience in AI/ML governance, compliance, technology risk, or a related regulatory function Working knowledge of AI-specific frameworks and legislation (NIST AI RMF, ISO 42001 & 23053, EU AI Act, and the proliferating landscape of US state-level AI legislation) Experience with policy or standards development, regulatory tracking, and compliance evidence/documentation Experience with GRC processes, data governance, or audit methodology is a plus Skills: Authoring and maintaining standards, processes, and governance documentation Regulatory tracking and translation of requirements into actionable AI standards and processes AI data governance: data provenance and lineage, PII handling, bias and representativeness, consent management Compliance evidence and audit-trail management AI inventory and model registry maintenance Governance tooling and automation (dashboards, audit trails, compliance checks) Cross-functional collaboration with Legal and Engineering Vacancy Status: This posting is for an existing vacancy. Base Salary: The anticipated base salary range for this position is CAD $95,000 to $135,000 annually. Final base salary will
Full responsibilities and requirements are on Mobility's application page.
Apply for this job →Location and market context
This job is based in Toronto, Canada on-site. Local candidates benefit from being close to Mobility's teams and regional hiring market. Confirm the exact in-office expectation and any relocation support with the employer.
About AI governance jobs
AI governance sits at the intersection of policy, risk, and engineering. Teams are standing up model inventories, use-case intake and review, risk classification, and control monitoring as regulation and board scrutiny of AI intensify. Jobs like this one are typically evaluated against frameworks such as NIST AI RMF, ISO/IEC 42001, the EU AI Act, and internal model-risk and privacy practices.
How to position yourself for this AI governance job
Strong candidates emphasize experience translating policy into operational controls, working across legal, compliance, security, product, and data teams, documenting AI system risks, and supporting governance processes. In your resume and outreach, tie your experience to how Mobility would apply NIST AI RMF, ISO/IEC 42001, the EU AI Act, and internal model-risk and privacy practices, and lead with concrete outcomes rather than duties.
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