Senior Artificial Intelligence Platform Engineer & Fabrics

BMO•Toronto, ON

24d•CA$75,900 - CA$141,900•Hybrid

About The Position

BMO is building the platform capabilities that make enterprise AI safe, governed, and scalable. We are seeking experienced Senior Engineers to help build and operate the core infrastructure that governs how AI runs at BMO: the AI Gateway, Policy Engine, Identity Fabric, AI Registry, Guardrails Runtime, and AI Observability. This is a build-and-run engineering role. You will design, implement, and operate significant components of these platform capabilities; writing production code, building integrations, and helping run what you. You will not build the AI models or applications themselves (those are domain-owned); you help build the governed platform they run on and the runtime evidence that proves they run within policy, across AWS, Azure, and Microsoft AI surfaces, under OSFI and OCC expectations. You are a hands-on engineer with a strong production track record who takes ownership of your components, cares about operability and correctness, and collaborates well within your team. You work within technical direction and standards set by the team's Principal Engineers and Leads, contribute to design discussions, and mentor more junior engineers. You are energized by hardening and scaling real engineering assets; an existing developer portal, an AI registry, a body of policy-as-code, and gateway integrations into enterprise-grade capabilities.

Requirements

Bachelor's degree in Computer Science, Software Engineering, or a related technical discipline (Master's an asset).
5+ years of software/platform engineering experience, including hands-on experience building and operating production services.
Experience with production operations (deployments, monitoring, incident response, on-call), ideally in a regulated industry (financial services an asset).
Hands-on depth in at least one of: API/gateway services; policy-as-code/authorization; workload identity/security; observability/telemetry; audit/data platforms.
Solid software-engineering fundamentals: clean, tested, maintainable code; API design; and an understanding of distributed-systems concepts (latency, resilience, availability).
Strong programming skills (Python and/or Go preferred; TypeScript/Java an asset) for building services, APIs, and integrations.
Working proficiency in cloud-native development on AWS and/or Azure: containers/Kubernetes and Infrastructure as Code (Terraform, CloudFormation/ARM).
Practical CI/CD, GitOps, and DevSecOps experience; Git-based workflows (Bitbucket/GitHub), Jira, Confluence.
Policy/Authorization: Cedar, OPA/Rego, or comparable authorization systems.
Identity/Security: SPIFFE/SPIRE, mTLS, OAuth/OIDC, token exchange, or federated identity.
Observability/Audit: OpenTelemetry, distributed tracing, Dynatrace/Splunk or equivalents, immutable/audit data stores.
Gateway/Guardrails: API gateway usage/development, LLM routing/abstraction, prompt-injection/PII defenses, or AI evaluation.
Registry/Portal: service catalogues, asset registries, lifecycle/onboarding workflows.
Working knowledge of GenAI platform patterns (LLM/AI gateways, RAG, agentic patterns, embeddings, guardrails) sufficient to build supporting infrastructure.
Awareness of Responsible AI, AI/data governance, privacy, cloud security, and IAM as applied to AI workloads.

Nice To Haves

Familiarity with AI/ML tooling (Bedrock, Azure OpenAI, SageMaker, MLflow) and orchestration frameworks (LangChain, LlamaIndex) an asset.
AWS Certified Solutions Architect – Associate (Professional an asset) / ML – Specialty
Microsoft Certified: Azure AI Engineer Associate / Azure Solutions Architect Expert

Responsibilities

Own and deliver components/features within your squad's capability, design (with guidance), implement, test, ship, and help operate in production.
Participate in on-call for the services your team runs.
Build APIs, MCP Servers, integrations, and tooling through which domains, DevOps pipelines, and enterprise systems consume platform capabilities.
Write clean, well-tested, well-instrumented code; build operability in from the start (metrics, tracing, SLO-aware design).
Ensure your components produce the runtime evidence connecting AI activity to policy enforcement, identity, and lineage for model-risk and regulatory review.
Contribute to design discussions and technical decisions, applying standards and patterns set by Principal Engineers and Leads.
Mentor junior engineers and collaborate actively across the squad.
Partner day-to-day with AI Developer Experience, AI Security, and AI SDLC counterparts as needed to deliver your work.