Technology Director, Enterprise AI Platform Engineering BMO is building a dedicated AI Engineering function to deliver the platform capabilities that make enterprise AI safe, governed, and scalable across our business domains and regulatory regimes. We are seeking an experienced technical leader to own the core infrastructure that governs and enforces how AI runs at BMO - the AI Gateway, Policy Engine, Identity Fabric, AI Registry, Guardrails Runtime, and AI Observability. You will lead a team that designs, ships, and operates the control and orchestration infrastructure sitting between policy authoring and inline enforcement — the capabilities every AI workload at BMO consumes to be secure, compliant, and observable.

You will not own the AI models or applications themselves (those are domain-owned); you own the governed platform they run on, and the runtime evidence that proves they run within policy. You are a hands-on technical leader who has built platform capabilities at scale, operates what you build, and designs for operability and regulatory defensibility from day one. You blend deep engineering credibility with the executive presence to partner across Security, Architecture, DevOps, and domain teams.

You are energized by taking real engineering assets — an existing developer portal, AI registry, a body of policy-as-code, and gateway integrations — and formalizing, scaling, and governing them into an enterprise-grade platform.

Enterprise Control Plane Developer

Portal & AI Registry - productionize the developer portal; deliver a federated AI Registry spanning agents, models, tools, channels, and evaluations, with self-service onboarding and lifecycle workflows.

Policy

Engine - policy-as-code infrastructure (Cedar/OPA), a policy compilation and GitOps distribution pipeline, risk-tiered approval workflows, and a policy simulation setting. Observability & Audit - a multi-pipeline architecture spanning operational, security, and compliance telemetry; cross-pipeline trace correlation; 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 multiple clouds; an inline enforcement engine with request-time policy evaluation, routing, residency, budget/quota controls, and circuit breakers, operating within strict latency budgets.

Guardrails

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

Identity

Fabric - workload identity for AI (SPIFFE/SPIRE), token-exchange bridging, per-domain trust boundaries, enterprise identity integration, and cross-cloud token federation with zero-trust attestation.



A production-hardened Developer Portal and federated AI Registry with sub-5-day self-service onboarding. An AI Gateway operational in a selected business domain, meeting tiered latency targets.

Policy-as-code infrastructure distributing domain-scoped policy bundles via GitOps, with a working simulation sandbox. A runtime evidence pipeline producing lineage-stamped, audit-ready traces aligned to model-risk and regulatory expectations. A team scaled from an initial core (8–12 FTE) toward steady-state through a blend of net-new hiring and reallocation of experienced internal engineers.

You design for operability and Engineering support from the start. Evidence-first - regulatory evidence (e.g., OSFI E-23, OCC model-risk expectations) is produced at runtime through instrumented infrastructure, not assembled retroactively. Organizational & People Leadership 8+ years in technical platform, infrastructure, or AI/ML engineering roles in a large enterprise, including 4+ years leading and managing engineering teams.

Proven organizational leadership: building and scaling engineering teams from a small core to steady-state, including workforce planning, hiring, succession planning, and structuring squads for clear ownership and accountability. Demonstrated team building across blended teams — integrating net-new hires with reallocated and seconded internal engineers into a single high-performing team with shared identity and standards.

Strong mentoring and coaching track record: developing engineers and technical leads, growing depth and bench strength, giving effective performance feedback, and creating clear technical growth pathways. Platform & AI Engineering Depth Demonstrated experience building and operating platform capabilities at scale — API gateways, policy/authorization systems, identity/workload-identity infrastructure, observability pipelines, or equivalent shared services.

Strong knowledge of GenAI platform engineering: LLM/AI gateways, model routing and abstraction, RAG and agentic patterns, guardrails, and AI evaluation approaches. Hands‑on experience with policy-as-code and authorization systems (Cedar, OPA/Rego, or equivalent) and GitOps-based distribution.

Experience with workload identity and zero‑trust patterns (SPIFFE/SPIRE, mTLS, token exchange, federated identity) — or strong adjacent identity/security engineering depth.

Strong observability engineering background: OpenTelemetry, distributed tracing, and telemetry pipelines across operational, security, and compliance domains. Multi‑cloud fluency (AWS and Azure preferred), cloud-native architecture, containerization/Kubernetes, and Infrastructure as Code.



Hands‑on familiarity with modern AI/ML tooling (e.g., Bedrock, Azure OpenAI, SageMaker, Databricks, MLflow, LangChain, or equivalents) sufficient to lead technical direction.

Governance, Strategy & Communication Solid grounding in Responsible AI, AI/data governance, privacy, and — ideally — model‑risk management and financial‑services regulatory expectations. Executive‑grade communication and relationship management across technical and senior‑leadership audiences (written, verbal, and presentation). Strategic and organizational management skills, including multi‑year roadmap planning, budgeting, forecasting, and vendor engagement in partnership with Vendor Management.

Bachelor's degree in Computer Science, Software Engineering, or a related technical discipline (Master's preferred).

Relevant certifications an asset: cloud (AWS/Azure/GCP) architecture or ML/AI certifications, Kubernetes (CKA/CKAD), security/identity certifications, or enterprise architecture (TOGAF or equivalent). 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. 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.

BMO is a leading bank driven by a single purpose: to Boldly Grow the Good in business and life. Everywhere we do business, we're focused on building, investing and transforming how we work to drive performance and continue growing the good.

Who we are We're proud to be fueling growth and expanding possibilities for individuals, families and businesses. More than 12 million customers count on us for personal and commercial banking, wealth management and investment services. As the 8th largest bank in North America by assets, we provide personal and commercial banking, wealth management and investment services to more than 12 million customers.

In Canada, the United States and across the globe, we'll continue to build, invest and transform to drive performance that serves the good that grows. #

📌 Director, AI Platform Engineering (Toronto)
🏢 Bank of Montreal
📍 Toronto