Technologie 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. 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 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. Enterprise AI Control Plane Portal & Registry: features of the AI Registry (agents, models, tools, channels, evaluations), self-service onboarding flows, and lifecycle workflows; Policy Engine: components of the policy-as-code infrastructure (Cedar/OPA), the compilation pipeline, GitOps-based distribution, and the policy simulation sandbox. Observability & Audit: telemetry pipeline components, OpenTelemetry GenAI instrumentation, trace correlation, and audit-lake ingestion supporting regulator-ready evidence. AI Domain Orchestration Gateway Runtime: components of the inline enforcement engine: request-time policy evaluation, routing, residency, budget/quota,



and circuit breaking, built to strict latency budgets; domain-hub deployment across AWS and Azure. Guardrails Runtime: stages of the safety pipeline (input moderation, prompt-injection defense, PII, output validation, hallucination detection, policy enforcement), including bilingual EN/FR support. Identity Fabric: components of workload identity for AI (SPIFFE/SPIRE), token-exchange flows, trust-boundary configuration, Entra Agent ID integration, and cross-cloud token federation. Two of these platforms (Identity Fabric, Policy Engine) are built with AI as the incubation context but are designed to transition to bank-wide ownership at maturity. Own and deliver components/features within your squad's capability, design (with guidance), implement, test, ship, and help operate in production. 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; 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. Partner day-to-day with AI Developer Experience, AI Security, and AI SDLC counterparts as needed to deliver your work.



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). policy-as-code/authorization; audit/data platforms. Platform Engineering Foundations Solid software-engineering fundamentals: clean, tested, maintainable code; Solid 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). Policy/Authorization: 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. Familiarity with AI/ML tooling (Bedrock, Azure OpenAI, SageMaker, MLflow) and orchestration frameworks (LangChain, LlamaIndex) an asset. Awareness of Responsible AI, AI/data governance, privacy, cloud security, and IAM as applied to AI workloads. AWS Certified Solutions Architect
- Associate (Professional an asset) / ML
- Specialty Microsoft Certified: Azure AI Engineer Associate / Azure Solut

📌 Senior platform engineer, ai (Canada)
🏢 BMO Financial Group
📍 Canada