Senior Cloud Engineer AI

Bank of Montreal•Virtual, DE

21h•Remote

About The Position

We are seeking a Senior Cloud Engineer with deep expertise in AWS and Azure AI/ML services to drive our enterprise ML/AI platform capabilities. You will evaluate and enable cloud AI/ML services, build reusable architectural patterns, and develop automated MLOps solutions in a highly regulated banking environment. This role requires hands-on experience with modern AI/ML platforms and the ability to design secure, compliant solutions that accelerate AI adoption across the organization.

Requirements

5-7 years of cloud engineering experience with 3+ years focused on AI/ML platforms
Deep hands-on expertise with AWS AI/ML services: SageMaker (training, pipelines, inference, JumpStart), Bedrock
Deep hands-on expertise with Azure AI/ML services: Azure Machine Learning, Azure OpenAI, Azure AI Foundry
Experience building MLOps platforms and automated ML pipelines
Strong knowledge of LLMOps, LLM lifecycle management, agentic AI, RAG (retrieval-augmented generation), and prompt engineering
Experience implementing guardrails and governance for LLM services
Proficiency in Python and infrastructure-as-code (Terraform, CloudFormation, ARM/Bicep)
Experience with MLflow(or similar tool), experiment tracking, and model registries
Expertise in cloud security patterns including private endpoints, customer-managed keys, and network isolation for AI/ML services
Strong understanding of cloud networking architecture in regulated environments
Experience working in highly regulated industries with compliance requirements
Agile delivery experience

Nice To Haves

AWS or Azure AI/ML certifications
Experience with vector databases and embedding models
Knowledge of model optimization and inference acceleration
Background in financial services or banking

Responsibilities

Evaluate and enable AWS and Azure AI/ML services (SageMaker, Bedrock, Azure OpenAI, Azure AI Foundry) through proof-of-concepts and comprehensive assessments
Design and implement reusable architectural patterns for secure AI/ML integrations including private endpoints, customer-managed keys, and service-to-service authentication
Build end-to-end MLOps platforms and automated ML pipelines for model training, evaluation, deployment, and monitoring
Produce technical reports on security, networking, compliance, guardrails, and cost analysis for AI/ML service enablement
Develop frameworks, infrastructure-as-code, and automation to accelerate AI/ML adoption
Implement observability solutions with model monitoring, metrics, and drift detection
Partner with Enterprise Architecture and senior stakeholders to align platform capabilities with strategic roadmaps
Provide technical leadership and mentorship on AI/ML cloud best practices