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Principal AI Cloud Engineer

BMO
Toronto, CAN
Full-time
Senior
Onsite
Discovered 3 weeks ago
azuregrafanakafkamlflowopentelemetryprometheus

Full Job Posting

Nice-to-Have

  • GPU optimization (CUDA/NCCL, TensorRT-LLM)
  • Observability tools (Prometheus, Grafana, OpenTelemetry)
  • Event streaming (Kafka/Azure Event Hubs), real-time systems
  • Experience with AI platform products (Azure ML, MLflow, KServe, Hugging Face)
  • Success Metrics
  • Reliability & Performance: SLOs met for infra services, GPU utilization optimized
  • Security & Compliance: Zero critical findings, auditable infra
  • Cost Efficiency: Reduced GPU/infra spend via FinOps strategies
  • Developer Velocity: Faster provisioning and deployment of AI infra
  • Technical Leadership: Influence on infra standards, mentorship, reusable patterns
  • Salary:
  • Pay Type:
  • 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
  • About Us
  • At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life. It calls on us to create lasting, positive change for our customers, our communities and our people. By working together, innovating and pushing boundaries, we transform lives and businesses, and power economic growth around the world.
  • As a member of the BMO team you are valued, respected and heard, and you have more ways to grow and make an impact. We strive to help you make an impact from day one – for yourself and our customers. We’ll support you with the tools and resources you need to reach new milestones, as you help our customers reach theirs. From in-depth training and coaching, to manager support and network-building opportunities, we’ll help you gain valuable experience, and broaden your skillset.
  • To find out more visit us at https://jobs.bmo.com/ca/en.
  • BMO is committed to an inclusive, equitable and accessible workplace. By learning from each other’s differences, we gain strength through our people and our perspectives. Accommodations are available on request for candidates taking part in all aspects of the selection process. To request accommodation, please contact your recruiter.
  • Note to Recruiters: BMO does not accept unsolicited resumes from any source other than directly from a candidate. Any unsolicited resumes sent to BMO, directly or indirectly, will be considered BMO property. BMO will not pay a fee for any placement resulting from the receipt of an unsolicited resume. A recruiting agency must first have a valid, written and fully executed agency agreement contract for service to submit resumes.

Responsibilities

  • Infrastructure & Platform Builder
  • Design, build, and operate cloud-native AI infrastructure for ML/GenAI workloads:
  • Compute: GPU/CPU clusters, autoscaling, spot instance strategies
  • Networking: Azure VNet, Private Link, peering, multi-region HA/DR
  • Storage & Databases: high-performance data lakes (e.g., Azure Data Lake Storage), relational DBs, vector DBs (FAISS, Milvus, Pinecone, pgvector)
  • Security: IAM, Key Vault-backed secrets management, encryption, policy-as-code
  • Implement observability and reliability for AI infra:
  • Metrics (latency, throughput, GPU utilization, cost)
  • Logging/tracing (OpenTelemetry), SLOs/SLIs for infra services
  • Build CI/CD and GitOps pipelines for infrastructure-as-code (Terraform/Bicep) and AI platform components
  • Drive FinOps for AI infra: GPU rightsizing, caching, inference optimization, cost governance
  • Application & Service Enablement
  • Enable frontend and backend services for AI platforms:
  • Secure APIs, microservices, and event-driven architectures
  • Integration with custom model runtimes (TensorRT-LLM, vLLM, Triton/KServe)
  • Provide infrastructure support for RAG systems: embeddings, chunking, retrieval pipelines
  • Ensure scalable serving infrastructure for LLMs and ML models with caching and token optimization
  • Strategy & Architecture
  • Define and evolve AI infrastructure reference architecture for cloud (Azure preferred):
  • Container orchestration (Kubernetes), service mesh, ingress
  • Serverless/event-driven patterns for AI pipelines
  • Multi-region, HA/DR, compliance-ready designs
  • Establish standards and best practices for containerization, IaC, and secure networking for AI systems
  • Security, Risk & Governance
  • Implement defense-in-depth for AI infra:
  • IAM least privilege, private networking, KMS/Key Vault, SBOM, image signing
  • Ensure compliance and Responsible AI controls at infra level:
  • Data residency, encryption, lineage, audit readiness
  • Delivery & Operations
  • Lead infrastructure discovery and solution design with stakeholders
  • Operate platforms with SRE principles: error budgets, incident response, chaos testing
  • Mentor engineers; create reusable IaC modules, templates, and golden paths
  • Must-Have Qualifications
  • Bachelor’s/Master’s/PhD in CS, Engineering, or related field
  • 7+ years building large-scale distributed cloud infrastructure
  • 5+ years hands-on with Azure/AWS
  • Proven experience with AI/ML infra: GPU clusters, Kubernetes, CI/CD, observability
  • Strong in IaC (Terraform/Bicep), Kubernetes, networking, security
  • Expertise in cloud-native patterns: containers, service mesh, serverless
  • Familiarity with MLOps/LLMOps infra: model serving, feature stores, vector DBs
  • Programming in Python (infra automation) and one of Go/TypeScript for tooling
  • Understanding of frontend/backend integration for AI services
  • Nice-to-Have
  • GPU optimization (CUDA/NCCL, TensorRT-LLM)
  • Observability tools (Prometheus, Grafana, OpenTelemetry)
  • Event streaming (Kafka/Azure Event Hubs), real-time systems
  • Experience with AI platform products (Azure ML, MLflow, KServe, Hugging Face)
  • Success Metrics
  • Reliability & Performance: SLOs met for infra services, GPU utilization optimized
  • Security & Compliance: Zero critical findings, auditable infra
  • Cost Efficiency: Reduced GPU/infra spend via FinOps strategies
  • Developer Velocity: Faster provisioning and deployment of AI infra
  • Technical Leadership: Influence on infra standards, mentorship, reusable patterns
  • Salary:
  • Pay Type:
  • 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

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