Work Location Toronto, Ontario, Canada

Hours 37.5

Line of Business Analytics, Insights, & Artificial Intelligence

Pay Details $154,000 - $199,500 CAD

This role is temporarily eligible for a pay premium above the posted salary range that is reassessed annually. You are encouraged to have an open dialogue with your recruiter who can provide more specific pay details for this role.

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate’s skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Job Description We are looking for an AI Platform Product Engineer who can lead AI initiatives from idea to production, and who can teach our engineers to build with AI. This person is technical enough to prototype agents and RAG solutions. They spend most of their time setting direction, running the backlog and getting results through others.

The role sits between our platform engineering teams and the business. It owns a portfolio of AI initiatives: it defines them precisely, prioritizes the work, removes blockers and proves value with measurable results. It also builds our engineers’ AI skills through hands‑on training,



live demos and reusable learning materials.

We care less about AI credentials and more about demonstrated skill. The strongest candidates can show us a clear task specification, an evaluation they designed, and a failure they caught before it reached production.

AI Program Leadership

Passionate about Generative AI and Machine Learning

AIOps/MLOps mindset enabling services and developing pipelines for managing training models and production models.

Own the vision, objectives and key results for a portfolio of AI initiatives

Build and continuously prioritize the AI product backlog, writing backlog items with clear acceptance criteria so engineers can commit with confidence.

Turn vague business requests into precise AI use cases with defined scope, constraints, success metrics and a clear definition of done.

Run AI work in short iterations: plan, review, accept or reject increments

Act as the main liaison between business stakeholders, engineering, security, risk and compliance, and keep senior leaders informed.

Identify and track cross-pod dependencies and risks, and keep a prioritized risk backlog.

Build business cases that show ROI, including the full cost of running AI in production (model usage, tokens, infrastructure and support).

A critical thinker with strong research and analytics skills





Self‑motivated with a positive attitude and an ability to work independently and in a team

AI Enablement for Engineers

Design and run a training program that teaches platform engineers and developers to build agents, multi‑agent workflows and RAG solutions.

Create demos, slide decks, hands‑on labs, sample code and reference architectures that engineers can reuse.

Run regular demo sessions, workshops and office hours that show recent AI techniques applied to our own platforms.

Teach engineers how to structure context for AI, evaluate AI output, recognize common failure patterns and keep costs under control.

Build a community of practice: share lessons learned, publish internal guides and grow AI champions in each team.

Measure the impact of training through skills assessments and adoption, and update the curriculum as the technology changes.

AI Platform Engineering

Build proofs of concept and prototypes for agents, RAG pipelines and multi‑agent workflows to test ideas before teams commit.

Design evaluation suites (test sets, automated evals, regression checks and metrics) for AI features before and after release.

Work with engineers on LLMOps practices: CI/CD for AI, monitoring, versioning and observability.

Apply AI security and guardrails, including prompt injection defenses, content filtering, access controls and human approval for high‑risk actions.

Make sure all AI solutions meet internal security, privacy, regulatory and responsible AI standards for financial services

Education & Experience

5+ years in cloud, platform engineering, technical product ownership or technical program management.

2+ y

📌 AI / ML Product Engineer (Winnipeg)
🏢 TD
📍 Winnipeg