BM Principal Cloud Engineer AI BMO · Locations · Cloud –11d ago

Final date to receive applications:

11/30/2026

Address:

320 S Canal Street

Job Family Group:

Data Analytics & Reporting

We accelerate BMO’s AI journey by building cloud-native AI solutions. Our team combines engineering excellence with cutting-edge AI to deliver scalable, secure, and responsible solutions that power business innovation across the bank. We enable and accelerate our partners on their AI journeys across the enterprise, helping teams across BMO unlock value  support one another in times of need and take pride in our work.

We are engineers, AI practitioners, platform builders, thought leaders, multipliers, and coders. Above all, we are a global team of diverse individuals who enjoy working together to create smart, secure, and scalable solutions that make an impact across the enterprise. Our ambition is bold: deploy our capital and resources to their highest and most profitable use through a digital-first operating model, powered by data and AI-driven decisions.

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. You’ll 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.

• 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.

• 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 Dev Sec Ops .
• Partner with a leadership team invested in your growth and thought leadership.

• 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 (Open Telemetry), SLOs/SLIs for infra services
• Build CI/CD and Git Ops pipelines for infrastructure-as-code (Terraform/Bicep) and AI platform components
• Drive Fin Ops for AI infra: GPU rightsizing, caching, inference optimization, cost governance

• 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

• Define and evolve AI infrastructure reference architecture for cloud (Azure preferred):
• Container orchestration (Kubernetes), service mesh, ingress
• Serverless/event-drive…