Senior Platform Engineer, AI & Fabrics

About Us

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, or a related technical discipline (Master's an asset).
• 5+ years of software/platform engineering experience, including hands-on experience building and operating production services.
• Experience with production operations (deployments, monitoring, incident response, on-call), ideally in a regulated industry (financial services an asset).
• Hands-on depth in at least one of: API/gateway services; policy-as-code/authorization; workload identity/security; observability/telemetry; audit/data platforms.
• Required Core Skills
• Platform Engineering Foundations
• Solid software-engineering fundamentals: clean, tested, maintainable code; API design; and an understanding of distributed-systems concepts (latency, resilience, availability).
• Working proficiency in cloud-native development on AWS and/or Azure: containers/Kubernetes and Infrastructure as Code (Terraform, CloudFormation/ARM).
• Practical CI/CD, GitOps, and DevSecOps experience; Git-based workflows (Bitbucket/GitHub), Jira, Confluence.
• Capability-Specific Depth (one or more)
• Policy/Authorization: Cedar, OPA/Rego, or comparable authorization systems.
• Identity/Security: SPIFFE/SPIRE, mTLS, OAuth/OIDC, token exchange, or federated identity.
• Observability/Audit: OpenTelemetry, distributed tracing, Dynatrace/Splunk or equivalents, immutable/audit data stores.
• Gateway/Guardrails: API gateway usage/development, LLM routing/abstraction, prompt-injection/PII defenses, or AI evaluation.
• Registry/Portal: service catalogues, asset registries, lifecycle/onboarding workflows.
• GenAI & Governance Context
• Working knowledge of GenAI platform patterns (LLM/AI gateways, RAG, agentic patterns, embeddings, guardrails) sufficient to build supporting infrastructure.
• Familiarity with AI/ML tooling (Bedrock, Azure OpenAI, SageMaker, MLflow) and orchestration frameworks (LangChain, LlamaIndex) an asset.
• Awareness of Responsible AI, AI/data governance, privacy, cloud security, and IAM as applied to AI workloads.
• AWS Certified Solutions Architect – Associate (Professional an asset) / ML – Specialty
• Microsoft Certified: Azure AI Engineer Associate / Azure Solutions Architect Expert
• $70,000.00 - $150,000.00
• Salaried
• The above represents BMO Financial Group’s pay range and type.
• Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group’s expected target for the first year in this position.
• 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. To view more details of our benefits, please visit: https://jobs.bmo.com/global/en/Total-Rewards

Preferred Qualifications

• Strong programming skills (Python and/or Go preferred; TypeScript/Java an asset) for building services, APIs, and integrations.
• Certifications (Preferred)

Responsibilities

• Own and deliver components/features within your squad's capability, design (with guidance), implement, test, ship, and help operate in production.
• Participate in on-call for the services your team runs.
• Build APIs, MCP Servers, integrations, and tooling through which domains, DevOps pipelines, and enterprise systems consume platform capabilities.
• Write clean, well-tested, well-instrumented code; build operability in from the start (metrics, tracing, SLO-aware design).
• Ensure your components produce the runtime evidence connecting AI activity to policy enforcement, identity, and lineage for model-risk and regulatory review.
• Contribute to design discussions and technical decisions, applying standards and patterns set by Principal Engineers and Leads.
• Mentor junior engineers and collaborate actively across the squad.
• Partner day-to-day with AI Developer Experience, AI Security, and AI SDLC counterparts as needed to deliver your work.