Director, AI Platform Engineering

About the Role

BMO is establishing a dedicated AI Engineering division responsible for delivering platform capabilities to ensure enterprise AI is secure, compliant, and scalable across various business units and regulatory frameworks. We are seeking a seasoned technical leader to oversee critical infrastructure components that govern AI operations including AI Gateway, Policy Engine, Identity Fabric, AI Registry, Guardrails Runtime, and AI Observability.

This is a leadership position involving both building and managing the ongoing operation of these infrastructures. The team you lead will design, deploy, and maintain control and orchestration mechanisms that act as the interface between policy formulation and enforcement, enabling secure, compliant, and observable AI workloads. The responsibility lies with the governed platform infrastructure rather than the AI applications or models themselves.

Key Responsibilities

• Lead the production and scaling of the Developer Portal and federated AI Registry, enabling self-service onboarding with lifecycle management of AI components.
• Manage the policy-as-code infrastructure, incorporating policy compilation, GitOps distribution, risk-based approval workflows, and a simulation environment.
• Oversee the development of a robust observability and audit framework featuring multi-pipeline telemetry architecture, OpenTelemetry GenAI standards, trace correlation, and tamper-proof audit logging to satisfy regulatory requirements.
• Implement governance and lifecycle processes including certification, automated compliance scoring, decommissioning protocols, and evidence generation for architecture and model risk assessments.
• Drive domain-specific orchestration with gateway runtime deployment across multi-cloud environments, enforcing inline policy evaluation, routing, budget controls, and latency management.
• Develop a multi-stage safety pipeline (Guardrails Runtime) addressing input moderation, prompt-injection defense, PII handling, output validation, hallucination detection, policy enforcement, ensuring functional parity in English and French.
• Build and maintain an identity fabric supporting workload identity via SPIFFE/SPIRE, token exchange, trust boundaries, enterprise identity integration, and cross-cloud federated authentication based on zero-trust principles.

Deliverables Within the First Year

• Release a hardened Developer Portal and federated AI Registry enabling onboarding within five days.
• Operate an AI Gateway within a selected domain meeting latency and performance standards.
• Deploy policy-as-code infrastructure utilizing GitOps and provide a functional policy simulation sandbox.
• Establish a runtime evidence pipeline delivering audit-quality traceability aligned with regulatory expectations.
• Expand the initial engineering team from 8–12 full-time members toward a full steady-state through hiring and redeployment.

Leadership and Working Approach

• Emphasize build-and-run integration where the team manages their developed infrastructure end-to-end.
• Maintain federated governance allowing domains to retain workload control while the platform provides enabling enforcement capabilities.
• Adopt an evidence-first mindset with real-time generation of regulatory compliance evidence through instrumentation.
• Focus on capability ownership across the platform ensuring cross-domain operability.

Required Expertise

• Over 8 years of experience in technical platform, infrastructure, or AI/ML engineering roles, including at least 4 years in engineering leadership or management within large enterprises.
• Proven capability in team growth, workforce planning, hiring, succession management, and building accountable team structures.
• Experience in integrating hybrid teams comprising new hires and internally reassigned engineers into cohesive high-performance units.
• Strong mentoring skills with track record of developing engineers and technical leads.
• Ability to foster an inclusive, respectful, and psychologically safe culture aligned with corporate values.
• Experience managing change and ambiguity in fast-evolving directives and tight deadlines.
• Competence in conflict resolution and collaborating with peer leaders on shared priorities.
• Hands-on experience building and operating scalable platform components such as API gateways, authorization systems, identity infrastructure, and observability pipelines.
• Deep knowledge of GenAI platform engineering including LLM gateways, model routing, retrieval-augmented generation (RAG), agentic AI patterns, and guardrails.
• Practical expertise with policy-as-code tools (e.g., Cedar, OPA/Rego) and GitOps mechanisms.
• Experience with workload identity, zero-trust security models, SPIFFE/SPIRE, mTLS, token exchange, and federated identity.
• Background in observability engineering utilizing OpenTelemetry, distributed tracing, and multi-domain telemetry collection.
• Multi-cloud proficiency (preferably AWS and Azure), cloud-native architecture, Kubernetes, containerization, and Infrastructure as Code.
• Familiarity with AI/ML tooling such as Bedrock, Azure OpenAI, SageMaker, Databricks, MLflow, LangChain.
• Proven expertise in CI/CD, DevSecOps, MLops, or LLMOps delivery frameworks.
• Understanding of Responsible AI, AI governance, privacy regulations, and financial services model risk management.
• Excellent communication skills for engaging both technical teams and executive leadership.
• Strategic planning aptitude including budgeting, forecasting, vendor management, and multi-year roadmap development.
• Strong analytical and problem-solving skills to handle complex and multi-stakeholder environments.
• Bachelor's degree in computer science, software engineering, or related technical field; Master's preferred.
• Additional certifications in cloud architecture, AI/ML, Kubernetes, security/identity, or enterprise architecture are advantageous.

Compensation and Benefits

The annual salary range for this position is $120,000 to $215,000, reflecting variation by skills, experience, location, and qualifications. The role is salaried and may include performance incentives, discretionary bonuses, and other rewards. Benefits at BMO include health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans.

About BMO

BMO is driven by a purpose to boldly grow positive impacts in business and life, fostering innovation and economic growth worldwide. The organization promotes an inclusive workplace culture valuing respect, diversity, employee development, and psychological safety. Accommodations are provided on request during the hiring process.