Senior Artificial Intelligence Platform Engineer, AI Platform & Fabrics
BMO Financial Group2 hours ago
Toronto, CanadaSenior
Base Salary
$76k - $142k/yr
Responsibilities
- Own and deliver production platform components within an AI platform squad, including design, implementation, testing, deployment, and operations.
- Build APIs, MCP servers, integrations, and tooling consumed by application domains, DevOps pipelines, and enterprise systems.
- Develop policy engines, identity fabric components, AI gateways, registries, guardrails, observability pipelines, lifecycle workflows, and audit-evidence tooling.
- Participate in on-call, deployments, monitoring, incident response, and operational improvements for team-owned services.
- Implement metrics, tracing, SLO-aware designs, and runtime evidence connecting AI activity to policy enforcement, identity, and lineage.
- Apply technical standards set by Principal Engineers and Leads, contribute to design decisions, mentor junior engineers, and collaborate with AI Developer Experience, AI Security, and AI SDLC teams.
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, or a related technical discipline is required; a master’s degree is an asset.
- At least 5 years of software or platform engineering experience, including hands-on production service development and operations.
- Experience with deployments, monitoring, incident response, on-call, and production operations; regulated financial-services experience is an asset.
- Strong software engineering fundamentals, API design knowledge, and understanding of distributed-systems concepts such as latency, resilience, and availability.
- Strong programming skills in Python and/or Go; TypeScript and Java are assets.
- Working proficiency with cloud-native development on AWS and/or Azure, including containers, Kubernetes, and infrastructure as code.
- Practical experience with CI/CD, GitOps, DevSecOps, Git-based workflows, Jira, and Confluence.
- Hands-on depth in at least one of API or gateway services, policy-as-code and authorization, workload identity and security, observability and telemetry, or audit and data platforms.
- Experience with authorization systems such as Cedar or OPA/Rego, identity technologies such as SPIFFE/SPIRE and OAuth/OIDC, observability tools such as OpenTelemetry and Dynatrace/Splunk, or API gateways and AI guardrails.
- Working knowledge of GenAI platform patterns including LLM gateways, RAG, agentic patterns, embeddings, and guardrails.
- Familiarity with Bedrock, Azure OpenAI, SageMaker, MLflow, LangChain, or LlamaIndex is an asset.
- Awareness of responsible AI, AI and data governance, privacy, cloud security, and IAM for AI workloads.
- Preferred certifications include AWS Certified Solutions Architect, AWS Certified Machine Learning Specialty, Microsoft Certified Azure AI Engineer Associate, or Azure Solutions Architect Expert.
Benefits
- Hybrid work model.
- Health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans.
- Training, coaching, manager support, and network-building opportunities.
- Inclusive, equitable, and accessible workplace with accommodations available upon request.
Tech Stack
Categories