Manager, AI Enablement

BMO•Toronto, ON

2d•CA$82,800 - CA$154,800•Hybrid

About The Position

The Manager, AI Enablement is a hands-on individual contributor responsible for enabling AI use-cases and production-grade AI capabilities across Canada P&BB Data & Analytics. The role partners closely with Product Owners, Business Architecture, Data & Analytics, and Technology teams to translate business needs into scalable, enterprise-grade AI solutions, while ensuring alignment to the capability roadmap and long-term architecture strategy.

Requirements

5-8+ years of experience in AI or analytics delivery
Strong hands-on Python/PySpark experience development for production
Experience with AWS and/or Azure ecosystems
Proven experience delivering production AI solutions
Strong ability to translate business needs into technical requirements and delivery plans
Solid understanding of the MLOps lifecycle, including: Model registry and versioning, CI/CD and deployment strategies (e.g. canary, shadow), Monitoring and reproducibility

Responsibilities

Partner with Product Owners to define, prioritize, and execute AI use cases aligned to business priorities and roadmap
Translate business problems into implementation-ready AI solutions, including workflows, data requirements, and models
Investigate new technologies and vendor solutions for AI use-cases
Partner with Business Architect to align AI initiatives to capability roadmap and target architecture
Ensure technology selections and implementation approaches support long-term strategy and scalability
Provide feedback into roadmap based on delivery insights and technical feasibility
Collaborate with platform, engineering, and other teams to design robust AI environments
Ensure alignment with enterprise standards and cloud strategies
Work across business, data, and engineering teams to deliver production-ready AI outcomes
Gather and translate business needs into clear functional and non-functional requirements (e.g., SLAs, data freshness, controls, auditability)
Act as a bridge between business stakeholders and technical teams, ensuring clarity and alignment throughout delivery
Partner with Technology Teams to enable production-grade MLOps pipelines, including model development, deployment, monitoring, and optimization
Support model lifecycle management (registry, versioning, CI/CD, environment promotion, reproducibility)
Support creation of scalable model inference capabilities across batch and/or real-time environments