MLOps / Cloud Deployment Engineer
Core
Own the deployment, reliability, observability, and operational scale of AI and agentic systems in a regulated enterprise environment.
Role type
Senior MLOps / Cloud Deployment Engineer
Builds
Production-ready CI/CD pipelines, observability stacks, and governance frameworks for ML/GenAI systems
Domain
Regulated enterprise finance / Cloud-native AI platforms
Deliverable
production ML models
Required skills
CI/CD pipeline management, cloud-native ML platform operations, observability and tracing, model governance and guardrails, infrastructure-as-code, cost and performance optimization, security posture management, evaluation framework implementation
Preferred skills
Agentic AI system operation, multi-agent orchestration, Kubernetes-native ML platforms, data warehouse compute governance
Technologies
AWS Bedrock, SageMaker, Azure AI Foundry, Azure Machine Learning, Terraform, Bicep, Docker, Kubernetes, MLflow, LangSmith, Weights & Biases, Python, Bash
Responsibilities
Design cost and performance optimization strategies for AI workloads, implement evaluation frameworks for AI systems in production, partner with data engineers and AI engineers to move systems from prototype to reliable production
Seniority
Senior, hands-on IC
