11/30/2026
Address:
320 S Canal Street
Job Family Group:
Data Analytics & Reporting
The Impact
As a Principal AI & Cloud Engineer, you are a hands-on technical developer who designs, builds, and scales cloud-native AI solutions and products. You help set engineering standards, establish patterns, mentor senior engineers, and partner with multiple teams to deliver resilient, governed, and cost-efficient AI at enterprise scale. Youll help shape and evolve our AI cloud strategy from model serving and LLMOps to security, observability, and compliance so teams across the bank can innovate safely and rapidly.
You will advance BMOs Digital First strategy by:
- Defining reference and production-grade solutions for AI/GenAI on cloud (Azure/AWS preferred; multi-cloud aware).
- Building reusable, secure, and observable components (APIs, SDKs, microservices, pipelines).
- Operationalizing LLMs and RAG with strong controls and Responsible AI guardrails.
- Driving platform roadmaps that enable faster delivery, lower risk, and measurable business outcomes.
Whats In It for You
- Influence the technical direction of AI and the platform primitives others build on.
- Ship high-impact systems used across many business lines and products.
- Work across the full stack: cloud infra, data/feature pipelines, model serving, LLMOps, and DevSecOps.
- Partner with a leadership team invested in your growth and thought leadership.
ResponsibilitiesInfrastructure & 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
- Bachelors/Masters/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
- 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:
$120,000.00 - $250,000.00
Pay Type:
Salaried
The above represents BMO Financial Groups 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 Groups expected target for the first year in this position.
BMO Financial Groups 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:
