Job description

Application Deadline: 11/30/2026 Address: 320 S Canal Street Job Family Group: Data Analytics & Reporting 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. What’s 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. 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 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 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: jobs.bmo.com 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 jobs.bmo.com BMO is proud to be an equal employment opportunity employer. We evaluate applicants without regard to race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or any other legally protected characteristics. We also consider applicants with criminal histories, consistent with applicable federal, state and local law. BMO is committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process, please send an e-mail to BMOCareers.Support bmo.com and let us know the nature of your request and your contact information. 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.

Requirements

Must-haves

320 S Canal Street
Data Analytics & Reporting
You will advance BMO’s Digital First strategy by:
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.
What’s 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.
Responsibilities Infrastructure & Platform Builder

Nice-to-haves

Defining reference and production-grade solutions for AI/GenAI on cloud (Azure/AWS preferred; multi-cloud aware).
Define and evolve AI infrastructure reference architecture for cloud (Azure preferred):
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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiazureawsmlci/cdllmkubernetespythongotypescript

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Health Insurance

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