Principal Cloud Engineer AI

BMO•Chicago, IL

6d•$120,000 - $250,000

About The Position

The Team - 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 at scale. We 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. 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. 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. You will advance BMO’s 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.

Requirements

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 Haves

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)

Responsibilities

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
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-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
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
Operate platforms with SRE principles: error budgets, incident response, chaos testing
Mentor engineers; create reusable IaC modules, templates, and golden paths