Application Deadline:10/29/2026Address:33 Dundas Street WestJob Family Group:TechnologyPrincipal Engineer, AI Platform &

• FabricsDescriptionBMO is building the platform capabilities that make enterprise AI safe, governed, and scalable. We are seeking experienced Principal/Senior engineers to 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 be part of the team that own Enterprise AI Platform capabilities end to end: Building, configuring and operating them in production, including on-call. You will not build the AI models or applications themselves (those are domain-owned); you 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 energized by taking real engineering assets that includes an existing developer portal, an AI registry, a body of policy-as-code, and gateway integrations and hardening, scaling, enhancing and governing them into enterprise-grade platform capabilities. You raise the technical bar for those around you and mentor as you build.Enterprise Control PlanePortal &

• Registry — a federated AI Registry (agents, models, tools, channels, evaluations across 16+ asset types) with self-service onboarding and lifecycle workflows

• Policy Engine — policy-as-code infrastructure (Cedar/OPA), a policy compilation pipeline, GitOps-based domain-scoped bundle distribution, risk-tiered approval workflows, and a policy simulation sandbox.Observability &

• Audit — a multi-pipeline telemetry architecture (operational + security + compliance), OpenTelemetry GenAI conventions, cross-pipeline trace correlation, lineage-stamped traces, and a 7-year tamper-evident audit lake producing regulator-ready evidence.Governance &

• Lifecycle — certification workflows, automated compliance scoring, decommission governance, and evidence generation for architecture and model-risk review.Domain OrchestrationGateway Runtime — domain-hub deployment across AWS and Azure; an inline enforcement engine performing request-time policy evaluation, routing, residency, budget/quota, and circuit breaking within strict tiered latency budgets (Fast <5ms / Standard <25ms / Full <50ms)

• Guardrails Runtime — a multi-stage safety pipeline (input moderation prompt-injection defense PII output validation hallucination detection policy enforcement) with bilingual EN/FR parity and behavioral guardrails for agentic workloads (goal hijacking,



intent drift, excessive agency).Identity Fabric — workload identity for AI (SPIFFE/SPIRE), token-exchange bridging, per-domain trust boundaries, Entra Agent ID integration, on-behalf-of identity propagation, and cross-cloud token federation with zero-trust attestation.What You'll DoOwn capabilities end to end: design, implement, test, ship, and operate production infrastructure.Engineer for operability and defensibility from day one: instrumentation, SLOs, latency budgets, failure modes, and runtime evidence built in, not bolted on.Build the APIs, SD'able interfaces, and integrations through which domains, DevOps pipelines, and enterprise systems consume platform capabilities.Implement policy enforcement, guardrails, identity attestation, and audit as first-class engineering concerns: correct, performant, and provable.Ensure every capability produces runtime evidence connecting AI activity to policy enforcement, identity, and lineage for model-risk and regulatory review (OSFI E-23, OCC).Assess emerging AI infrastructure, foundation-model access patterns, and standards; make deliberate, cost-aware engineering choices.Mentor and raise the bar: set engineering standards, review designs and code, and grow depth across the team.Partner closely with AI Developer Experience , AI Security and AI SDLC , and the Senior AI Architect in your platform build and operations.Education &

• ExperienceBachelor's degree in Computer Science, Software Engineering, or a related technical discipline (Master's preferred).8+ years of software/platform engineering experience (Principal), or 5+ years (Senior), with substantial time building and operating shared platform services at enterprise scale.policy-as-code and authorization; observability and telemetry pipelines; audit/compliance data platforms.Required Core SkillsPlatform Engineering DepthStrong distributed-systems and platform-engineering fundamentals: latency-sensitive request paths, resilience patterns (circuit breakers, failover), multi-tenancy, and high availability.Robust programming skills (Python and/or Go preferred

• TypeScript/Java an asset) for building performant services, APIs, and integrations.Cloud-native architecture across AWS and Azure : containers/Kubernetes, service mesh,



and Infrastructure as Code (CDK, Terraform, CloudFormation/ARM).Capability-Specific Depth (one or more)Policy/Authorization: Cedar, OPA/Rego, policy compilation and distribution, risk-tiered approval workflows.GenAI conventions), distributed tracing, Dynatrace/Splunk or equivalents, tamper-evident/immutable audit stores, data lineage.Gateway/Guardrails: API gateway internals, inline enforcement, LLM routing/abstraction, prompt-injection and PII defenses, hallucination detection, AI evaluation.Registry/Portal: service catalogues, asset registries, lifecycle workflows, federation with external registries.LLM/AI gateways, RAG and agentic patterns, foundation models, embeddings, and guardrails — sufficient to build the infrastructure they depend on.Familiarity with AI/ML platforms (Bedrock, Azure OpenAI, SageMaker, MLflow) and orchestration frameworks (LangChain, LlamaIndex).Grounding in Responsible AI, AI/data governance, privacy, cloud security, and IAM as applied to AI workloads.Certifications (Preferred)AWS Certified Solutions Architect (Associate/Professional) / ML - SpecialtyMicrosoft Certified: Azure Solutions Architect Expert / Azure AI Engineer AssociateSecurity/identity certifications (relevant to Identity Fabric roles)Databricks / Google Cloud ML credentials; relevant GenAI/LLM credentialsOther SkillsStrong communication and collaboration across engineering, security, architecture, and domain teams.participates in on-call rotation for owned services.Salaries for part-time roles will be pro-rated based on number of hours regularly worked. BMO Financial Group’s total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. It calls on us to create lasting, positive change for our customers, our communities and our people. We strive to help you make an impact from day one - for yourself and our customers. We’ll support you with the tools and resources you need to reach new milestones, as you help our customers reach theirs. From in-depth training and coaching, to manager support and network-building opportunities, we’ll help you gain valuable experience, and broaden your skillset.Accommodations are available on request for candidates taking part in all aspects of the selection process. A recruiting agency must first have a valid, written and fully executed agency agreement contract for service to submit resumes.

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📌 General Engineering Application (Toronto)
🏢 Bank of Montreal
📍 Toronto