Director AI Platform Engineering

Application Deadline:

10/29/2026

Address:

320 S Canal Street

Job Family Group:

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.

This is a build-and-run leadership role. 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.

What You'll Own

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 environment.

Observability & Audit - a multi-pipeline architecture spanning operational, security, and compliance telemetry; OpenTelemetry GenAI conventions; cross-pipeline trace correlation; and a 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 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.

What You'll Deliver (First 12 Months)

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.

How You'll Work

Build-run integrated - your team operates what it builds; there is no separate run team. You design for operability and Engineering support from the start.

Federated - you own the enforcement infrastructure domains consume; domains own their workloads. You enable, you don't centralize execution.

Evidence-first - regulatory evidence (e.g., OSFI E-23, OCC model-risk expectations) is produced at runtime through instrumented infrastructure, not assembled retroactively.

Capability-aligned - your teams own outcomes ("Identity Fabric works across all platforms"), not specific technologies.

Required Core Skills

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.

Ability to establish and sustain a healthy, inclusive team culture that promotes psychological safety, mutual respect, recognition, and employee engagement, aligned to BMO Values.

Experience leading through change and ambiguity — standing up a new function, aligning a team to a fast-evolving mandate, and maintaining momentum under tight timelines.

Conflict resolution and cross-team influence, including partnering with peer Directors on shared roadmaps and resolving competing priorities.

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.

Proven CI/CD, DevSecOps, and MLOps/LLMOps delivery experience.

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.

A critical thinker with strong analytical, problem-solving, and prioritization abilities across a complex, multi-stakeholder portfolio.

Education & Certifications

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, Kube