Principal Engineer AI

Principal Engineer, AI Platform & Fabrics

Description

BMO 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 a hands-on engineer who has built shared platform services at scale, cares deeply about operability, latency, and correctness, and understands that in a regulated bank the infrastructure must produce its own evidence. 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.

What You'll Build & Operate

Depending on your specialization, you will own one or more of the following capability areas:

Enterprise Control Plane

• Portal & Registry — a federated AI Registry (agents, models, tools, channels, evaluations across 16+ asset types) with self-service onboarding and lifecycle workflows; federation with external registries (Agent 365, AgentCore, MLflow).

• 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 Orchestration

• Gateway 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); cross-region failover.

• 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 Do

• Own 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 & Experience

• Bachelor'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.

• Demonstrated experience operating production infrastructure with real SLOs and on-call ownership, ideally in a regulated industry (financial services strongly preferred).

• Depth in one or more of: API gateways / traffic enforcement; policy-as-code and authorization; workload identity / zero-trust; observability and telemetry pipelines; audit/compliance data platforms.

Required Core Skills

Platform Engineering Depth

• Strong distributed-systems and platform-engineering fundamentals: latency-sensitive request paths, resilience patterns (circuit breakers, failover), multi-tenancy, and high availability.

• Strong 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).

• Robust CI/CD, GitOps, and DevSecOps practice; Git-based workflows (Bitbucket/GitHub), Jira, Confluence.

Capability-Specific Depth (one or more)

• Policy/Authorization: Cedar, OPA/Rego, policy compilation and distribution, risk-tiered approval workflows.

• Identity/Zero-Trust: SPIFFE/SPIRE, mTLS, token exchange, OAuth/OIDC, federated and cross-cloud identity, Entra ID/Agent ID.

• Observability/Audit: OpenTelemetry (incl. 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.

GenAI & Governance Context

• Working knowledge of GenAI platform patterns: 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 – Specialty

• Microsoft Certified: Azure Solutions Architect Expert / Azure AI Engineer Associate

• Security/identity certifications (relevant to Identity Fabric roles)

• Databricks / Google Cloud ML credentials; relevant GenAI/LLM credentials

Other Skills

• Strong communication and collaboration across engineering, security, architecture, and domain teams.

• A critical thinker with strong analytical and problem-solving skills.

• Self-directed, comfortable with ambiguity and a fast-evolving mandate.

• Able to deliver complex work under tight timelines; participates in on-call rotation for owned services.