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About the Role
BMO is establishing a dedicated AI Engineering function to create a scalable, governed, and safe enterprise AI platform. This role leads the design, development, and operation of core infrastructure for AI governance and execution, including the AI Gateway, Policy Engine, Identity Fabric, AI Registry, Guardrails Runtime, and AI Observability. You will own the platform that AI workloads run on, ensuring they operate within policy and compliance.
Responsibilities
- Design, ship, and operate the control and orchestration infrastructure for AI governance.
- Productionize the developer portal and deliver a federated AI Registry.
- Implement policy-as-code infrastructure with a GitOps distribution pipeline.
- Develop multi-pipeline observability and audit capabilities for operational, security, and compliance telemetry.
- Establish certification workflows, automated compliance scoring, and decommission governance.
- Deploy an AI Gateway across multiple clouds with request-time policy evaluation and latency controls.
- Implement a multi-stage safety pipeline for guardrails and behavioral controls for agentic workloads.
- Build a workload identity fabric for AI using zero-trust principles.
- Deliver a production-hardened Developer Portal and federated AI Registry within sub-5-day onboarding.
- Operationalize an AI Gateway in a selected business domain meeting tiered latency targets.
- Distribute domain-scoped policy bundles via GitOps with a working simulation sandbox.
- Produce a runtime evidence pipeline for lineage-stamped, audit-ready traces.
- Scale the team from an initial core to steady-state through hiring and reallocation.
Requirements
- 8+ years in technical platform, infrastructure, or AI/ML engineering roles in a large enterprise.
- 4+ years leading and managing engineering teams.
- Proven organizational leadership in building and scaling engineering teams.
- Demonstrated team building across blended teams.
- Strong mentoring and coaching track record.
- Experience establishing and sustaining a healthy, inclusive team culture.
- Experience leading through change and ambiguity.
- Conflict resolution and cross-team influence skills.
- Demonstrated experience building and operating platform capabilities at scale.
- Strong knowledge of GenAI platform engineering.
- Hands-on experience with policy-as-code and authorization systems.
- Experience with workload identity and zero-trust patterns.
- Strong observability engineering background.
- Multi-cloud fluency (AWS and Azure preferred), cloud-native architecture, containerization/Kubernetes, and Infrastructure as Code.
- Hands-on familiarity with modern AI/ML tooling.
- Proven CI/CD, DevSecOps, and MLOps/LLMOps delivery experience.
- Solid grounding in Responsible AI, AI/data governance, privacy, and model-risk management.
- Executive-grade communication and relationship management skills.
- Strategic and organizational management skills, including roadmap planning, budgeting, and forecasting.
- Critical thinker with strong analytical, problem-solving, and prioritization abilities.
Skills
- API gateways
- Policy/authorization systems
- Identity/workload-identity infrastructure
- Observability pipelines
- GenAI platform engineering
- LLM/AI gateways
- Model routing and abstraction
- RAG and agentic patterns
- Guardrails
- AI evaluation approaches
- Policy-as-code
- Authorization systems (Cedar, OPA/Rego)
- GitOps
- Workload identity
- Zero-trust patterns (SPIFFE/SPIRE, mTLS, token exchange, federated identity)
- Observability engineering
- OpenTelemetry
- Distributed tracing
- Telemetry pipelines
- Multi-cloud architecture (AWS, Azure)
- Cloud-native architecture
- Containerization
- Kubernetes
- Infrastructure as Code
- AI/ML tooling (Bedrock, Azure OpenAI, SageMaker, Databricks, MLflow, LangChain)
- CI/CD
- DevSecOps
- MLOps/LLMOps
- Responsible AI
- AI/data governance
- Privacy
- Model-risk management
- Financial-services regulatory expectations
- Communication
- Relationship management
- Strategic planning
- Organizational management
- Budgeting
- Forecasting
- Vendor engagement
- Analytical skills
- Problem-solving
- Prioritization
Location
- 320 S Canal Street
Work Type
- Salaried
Experience Level
- 8+ years in technical platform, infrastructure, or AI/ML engineering roles
- 4+ years leading and managing engineering teams
Education Level
- 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).
Salary/Compensations
- $150,700.00 - $261,800.00
Benefits
- Health insurance
- Tuition reimbursement
- Accident and life insurance
- Retirement savings plans
About the Company
- BMO is driven by a shared Purpose: Boldly Grow the Good in business and life.
- We aim to create lasting, positive change for our customers, our communities, and our people.
- We foster an environment where employees are valued, respected, and heard, with opportunities for growth and impact.
- We provide the tools and resources needed for employees to reach new milestones and support customers in reaching theirs.
Equal Opportunity
- BMO is proud to be an equal employment opportunity employer.
- We evaluate applicants without regard to race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or any other legally protected characteristics.
- We also consider applicants with criminal histories, consistent with applicable federal, state and local law.
- BMO is committed to working with and providing reasonable accommodations to individuals with disabilities.