AWS ML Ops Engineer

Job Summary The AWS ML Ops Engineer is responsible for designing, building, and deploying cloud-native AI/ML solutions on AWS. This role focuses on developing agent-based systems, managing model lifecycle processes, and implementing scalable MLOps practices to support production-ready AI applications. Key Responsibilities Design, build, and deploy cloud-native applications on AWS Develop and implement agent-based (agentic) systems and workflows Write and deploy production-ready agent code Support MLOps processes including model deployment, monitoring, and iteration Define and manage infrastructure using Terraform (Infrastructure as Code) Prototype and experiment with emerging technologies and frameworks Collaborate with cross-functional teams to deliver scalable AI/ML solutions Evaluate and adopt new tools to improve system performance and efficiency Required Qualifications 36+ years of experience in software engineering Strong hands-on experience with AWS development and deployment Experience with MLOps practices including model lifecycle, CI/CD, and monitoring Proficiency in Terraform and Python Experience building or working with agent-based or agentic systems Experience writing and deploying agent code in production environments Familiarity with agent frameworks and related concepts Exposure to Model Context Protocol (MCP) or similar paradigms Preferred Qualifications Experience with Databricks Experience with AWS AI/ML services such as Bedrock or similar platforms Education: Bachelors Degree