Job Description

Group Lead, AI Governance to establish and operate the technical governance, platform controls and support model that enable safe, responsible and scalable use of AI across the enterprise. Based in Toronto, this leader will connect policy to day-to-day engineering practice across enterprise productivity AI, low-code agents and production-grade AI/ML solutions. This is a hands-on leadership role (approximately 60% execution and 40% strategy). You will own the control framework and operating routines for platforms such as Microsoft 365 Copilot, Claude Enterprise, Microsoft Copilot Studio, Azure AI services and engineering patterns built with frameworks and observability tools such as LangGraph and LangSmith. You will partner with AI engineering, MLOps, security, legal, privacy, enterprise architecture, product and business teams to make risk-based decisions, remove delivery friction and ensure controls remain effective throughout the AI lifecycle.

Group Lead, AI Governance at a glance

Location: Toronto, ON

Hybrid: 3 days/week

What's on the menu?

- Lead the technical AI governance and platform support function, setting priorities, service expectations, operating routines and clear accountability across enterprise AI tools and custom AI solutions.
- Translate AI policies, risk appetite and regulatory obligations into practical engineering standards, approval criteria, platform configurations, reusable controls and evidence requirements.
- Own risk-tiered lifecycle governance from intake and experimentation through production approval, change management, monitoring, incident response and retirement, with proportionate checkpoints based on use-case criticality.
- Establish and maintain an enterprise inventory of AI tools, models, agents,



use cases, owners, data dependencies, risk classifications, approvals and material changes.
- Support relevant teams on technical controls for identity and access, least privilege, data protection, secrets management, workplace separation, vendor integrations, human oversight, logging, traceability and audit evidence.
- Set standards for AI evaluation and production readiness, including quality, safety, reliability, security, privacy, latency, cost and business acceptance criteria; ensure exceptions are documented, time-bound and remediated.
- Build observability and governance patterns for agentic systems, including prompt and response tracing, tool-call visibility, evaluation datasets, failure analysis, escalation paths and controls for autonomous actions.
- Govern and support AI platforms including Microsoft 365 Copilot, Claude Enterprise, Copilot Studio, Azure-based AI services and custom solutions using LangGraph, LangSmith and related MLOps/LLMOps capabilities.
- Lead AI platform intake, access and support processes; triage incidents and control gaps, coordinate root‑cause analysis, track corrective actions and communicate material risks to the appropriate governance bodies.
- Partner with Legal, Privacy, Cybersecurity, Procurement, Internal Audit, Data Governance, Enterprise Architecture and business owners on use-case reviews,



third-party assessments, control testing and audit readiness.
- Define governance metrics and service health indicators covering inventory completeness, review cycle time, control compliance, incidents, exceptions, evaluation coverage, adoption, consumption and remediation progress.
- Lead cross-functional initiatives and technical projects, managing scope, milestones, dependencies, decisions and stakeholder communications while coaching engineers and analysts on responsible AI practices.
- Monitor developments in AI regulation, standards, platform capabilities and emerging risks; assess their relevance and drive updates to controls, playbooks and technical roadmaps.

- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Technology, Cybersecurity, or related field (advanced degree an asset)
- 7+ years of experience across MLOps, machine learning engineering, data science, cloud engineering, platform engineering, technology risk, or related disciplines, including responsibility for production AI/ML systems
- Hands-on experience designing, deploying, operating, or governing AI/ML platforms in a large, matrixed enterprise environment
- Practical understanding of AI risk management and responsible AI principles (accountability, transparency, privacy, security, reliability, safety, fairness, human oversight)
- Experience with cloud and identity controls, CI/CD, model/agent lifecycle management, observability, incident management, and production support (preferably Microsoft Azure)
- Experience with generative AI and agentic patterns (LLM applications, RAG, prompt/context engineering, tool use, agent workflo

📌 Group Lead, AI Governance (Canada)
🏢 Kraft Heinz
📍 Canada