Senior Cloud Engineer AI
Bank of Montreal (BMO) is a financial services company providing personal banking, business banking, wealth management, commercial banking, capital markets, and global asset management services. Its personal banking offerings include accounts, credit cards, loans, mortgages, investments, digital banking, and financial education.
About Bank of Montreal (BMO)
Bank of Montreal (BMO) provides a broad range of financial products and services for personal, business, commercial, institutional, and wealth-management clients. Personal offerings include checking and savings accounts, credit cards, loans and lines of credit, mortgages, investments and retirement products, digital banking, payment services, fraud protection, and financial education. The website also identifies BMO Wealth, Commercial, Capital Markets, and Global Asset Management divisions.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will evaluate AWS and Azure AI/ML services, design secure reusable integration patterns, and build automated MLOps platforms. You will develop infrastructure automation, implement model observability and drift detection, produce technical assessments, and mentor others on cloud AI/ML practices.
Requirements
- 5–7 years of cloud engineering experience, including 3+ years focused on AI/ML platforms
- AWS AI/ML services, including SageMaker and Bedrock
- Azure Machine Learning, Azure OpenAI, and Azure AI Foundry
- MLOps platforms and automated ML pipelines
- LLMOps, LLM lifecycle management, agentic AI, RAG, and prompt engineering
- LLM guardrails and governance
- Python
- Terraform, CloudFormation, or ARM/Bicep
- MLflow, experiment tracking, and model registries
- Cloud security, private endpoints, customer-managed keys, and network isolation
- Cloud networking architecture
- Experience in regulated industries and Agile delivery
Responsibilities
- Evaluate AWS and Azure AI/ML services through proof-of-concepts and assessments
- Design secure reusable architectural patterns for AI/ML integrations
- Build MLOps platforms and automated ML pipelines
- Produce technical reports on security, networking, compliance, guardrails, and cost
- Develop infrastructure-as-code frameworks and automation
- Implement model monitoring, metrics, and drift detection
- Align platform capabilities with strategic roadmaps
- Provide technical leadership and mentorship
Benefits
- Health insurance
- Tuition reimbursement
- Accident insurance
- Life insurance
- Retirement savings plans
