Senior Cloud Engineer – ML/AI Platform

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