AI Engineer Job Mississauga area,Ontario Canada,IT/Tech

Role:

AI Engineer

Location:

Mississauga, Ontario – Hybrid (4 days/week in office)

* Moving to Etobicoke in 2026*

Company:

Mattamy Asset Management

Department:

Information Technology

Employment Type:

Full-Time

Reports to:

Director, Platform Engineering

At Mattamy Asset Management (MAM), everyone has an important role to play in our shared success. Developing thoughtfully planned communities is complex work and our diverse teams come together to deliver on that mission in every aspect. We are thoughtful planners, precise project managers and practiced subject matter experts. And in each area of our evolving business, we are trusted to drive results.

Here, your opinion will be invited, and your contributions will count. You’ll be surrounded by caring people who encourage you to be exactly who you are. You’ll grow in your area of expertise, learning alongside committed colleagues. With a relentless focus on industry leadership and a deep commitment to sustainability, we’ve got big plans for the future – and for you.

Learn more about what makes working at Mattamy special and our award-winning culture.

What we offer

In this role, you’ll lead the design, build, and run of agentic AI solutions that change how a complex, enterprise-grade business operates.

This role operates as part of Mattamy’s enterprise AI team, working closely with AI product leadership and platform teams as the AI capability scales. You’ll own the technical architecture for assigned AI solutions across RAG pipelines, LLM configuration, system integrations, and data flows, and you’ll contribute to the technical standards and reference patterns across internal and partner-led builds. This is an ideal opportunity for a senior AI engineer who wants end-to-end ownership — solution design through production run — and the chance to shape an AI program as it scales.

What you’ll do

Lead the architecture and design of agentic AI solutions, including RAG pipelines, LLM configuration, prompt and tool design, system integrations, and end-to-end data flows

Own technical quality and engineering standards across all AI builds, in alignment with the enterprise AI architecture and governance framework— code review, design review, evaluation criteria, and production readiness

Write production-grade code as a hands-on contributor — prototype quickly, ship working solutions end-to-end, and stay close to the implementation details to make AI systems reliable

Build and maintain core AI components hands-on — retrieval pipelines, agent orchestration, and integrations with enterprise systems and data sources

Own production run of deployed AI solutions — observability, evaluations, incident response, and ongoing tuning for accuracy, latency, cost, and drift

Establish and maintain architecture documentation, reference patterns, and runbooks so the program can scale across multiple concurrent builds

Embed responsible AI practices into every build — data privacy, access controls, prompt and output safety, evaluation, and human-in-the-loop where it matters

Partner with business, data, and platform teams to translate prioritized AI use cases into production-ready AI solutions that deliver measurable outcomes

What you bring

5+ years of software engineering experience, including 2+ years building production AI/LLM solutions, or an equivalent combination of education and experience

Hands-on experience building on Azure AI Foundry (or equivalent) with OpenAI / Azure OpenAI and Anthropic Claude — prompt design, tool/function calling, structured outputs, and managing cost, latency, and token limits

Proven experience designing and shipping RAG pipelines — chunking, embeddings, vector stores, hybrid retrieval, re-ranking, and grounding strategies that hold up in production

Experience leading agent architecture — orchestration, tool use, multi-step reasoning, memory, and integration with enterprise systems and data sources

Strong software engineering fundamentals — Python or TypeScript, APIs, version control (Git), CI/CD, and Agile delivery tools (e.g., Azure Dev Ops)

Experience running AI solutions in production — evaluation frameworks, observability, regression testing, and tuning for accuracy,…