Manager, AI Product & Governance – Commercial Banking

BMO•Toronto, ON

2d•CA$75,900 - CA$141,900

About The Position

As the Manager, AI Product & Governance, you will play a hands-on role in shaping the future of an AI-enabled Commercial Banking environment. Reporting to the Senior Manager, AI Strategy, Product & Governance, you won't just be checking boxes, you will actively operationalize the guardrails and champion agile product delivery that allow us to scale AI solutions effectively, safely and responsibly. Deploying AI effectively provides significant and sustainable value to our business, but scaling responsibly requires disciplined execution and forward-thinking guardrails. In this role, you are the dual champion for both AI Risk Management and Internal AI Product Excellence. You will ensure that safety and innovation go hand-in-hand, directly influencing how our teams and clients experience the next generation of banking technology.

Requirements

Experience working within governance, controls, or regulatory frameworks, with the ability to track business outcomes and support disciplined, compliant delivery
Demonstrated experience supporting end-to-end product delivery, from concept through to implementation
5+ years of experience in AI product delivery, AI/analytics, product management, and/or AI governance within financial services.
Experience supporting end-to-end delivery of AI or data-driven solutions, including use case development, solution design, implementation, and cross-functional execution.
Working knowledge of AI/ML concepts, data pipelines, and lifecycle management, with the ability to apply technical concepts in a practical business context.
A proactive and detailed oriented problem solver who confidently navigates complex challenges, with the ability to identify issues, escalate risks, and contribute to process improvements
Strong organizational skills with the ability to manage multiple initiatives, timelines, and dependencies simultaneously
Effective communication skills, with the ability to translate technical concepts into clear, business-friendly language
Experience working with cross-functional stakeholders (Business, Risk, Technology, Data Science), with the ability to build and maintain strong working relationships

Responsibilities

Support the implementation and updates of the group level AI Risk governance program in accordance with the internal and external regulatory requirements
Assist in the operationalization of the group level AI governance framework, including intake, triage, documentation tracking, approvals, controls, monitoring, and reporting to remain in compliance with AI risk requirements
Embody the best AI Risk governance practice by working with product owners and internal stakeholders to ensure required artifacts and controls are in place for each AI use cases and well documented
Maintain and update inventory and trackers, providing regular status updates on use cases, risks, issues, and control coverage
Drive continuous improvement of AI governance processes by identifying gaps, enhancing workflows, reporting, and promoting consistent adoption of best practices across use cases
Conduct risk-informed review of AI use cases to identify potential issues, validation requirements, and areas requiring further attention or escalation
Support governance clearance and oversight for selected internally built AI use cases by ensuring required controls, performance metrics, KPI tracking, and stakeholder coordination are in place
Support the delivery of AI use cases by coordinating collaboration across business, technology, data, operations, and risk stakeholders
Contribute to end-to-end product execution, including planning, testing, pilot activities, change readiness, and end-user enablement
Translate AI concepts, outputs, and requirements into clear, business-friendly materials that support decision-making and stakeholder engagement.
Partner with Risk and Control teams to support governance, compliance, documentation, and control requirements throughout the delivery lifecycle
Help maintain consistent delivery practices by supporting performance tracking, playbooks, templates, standards, and incorporation of end-user feedback into solution development