MLOps / Cloud Deployment Engineer

Our Client's Digital Finance IT is scaling AI and agentic systems in production. We need an MLOps / Cloud Deployment Engineer to own the deployment, reliability, observability, and operational scale of these systems in a regulated enterprise environment.

This is a cloud and platform engineering role with deep MLOps/LLMOps focus, not a model-building role. You will operate the runway that ML and GenAI systems run on, not build the models themselves.

What You'll Do

• Own CI/CD pipelines for ML models, RAG applications, and agentic AI systems — from experiment to production
• Deploy and operate AI workloads on cloud-native ML/AI platforms — AWS Bedrock/SageMaker, Azure AI Foundry / Azure Machine Learning, or equivalent
• Build and maintain observability, tracing, and monitoring for LLM and agentic systems — latency, cost, hallucination rates, tool-call success, drift detection
• Implement model governance and guardrails — approval gates, kill-switches, escalation paths, audit trails
• Manage infrastructure-as-code (Terraform, Bicep, or equivalent) for reproducible AI/ML environments
• Design cost and performance optimization strategies — token usage tracking, caching, model routing, autoscaling, warehouse/cluster right-sizing
• Own security posture — RB