Senior Cloud Engineer – MLAI Platform

Role: Senior Cloud Engineer ML/AI Platform

Location: Toronto ON
Contract

About the Role

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.

What You Will Do

• 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

What You Need to Succeed

Must Have

• 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 Have

• 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