ML Ops and LLM Ops

MLOps and LLMOps Engineer - AWS

You will be responsible for designing, building, and operating scalable MLOps and LLMOps platforms on AWS, enabling Machine Learning, GenAI, and Agentic AI solutions to move reliably from development into production.

• Design and implement end-to-end MLOps and LLMOps pipelines on AWS.
• Build automated pipelines for model/LLM training, evaluation, versioning, deployment, monitoring, and rollback.
• Develop scalable deployment patterns for ML models, LLM applications, RAG and Agentic AI solutions.
• Build and maintain CI/CD pipelines for ML and GenAI workloads.
• Develop reusable Infrastructure as Code (IaC) components for cloud infrastructure and AI platforms.
• Containerize and orchestrate ML/LLM workloads using Docker and Kubernetes/EKS.
• Implement orchestration workflows using Argo Workflows and related cloud-native technologies.
• Establish production monitoring and observability for models and LLM applications, covering latency, quality, failures, resource utilization, and cost.
• Enable secure integration of foundation models and GenAI services using Amazon Bedrock and related AWS services.
• Work closely with Data Scientists, AI Engineers, Software Engineers, Cloud/DevOps teams, and architecture stakeholders to industrialize AI solutions.
• Support production operations, troubleshooting, scalability, reliability, security, and continuous improvement of AI services.

Education

University degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related technical field. Ideally specialization or hands-on project experience in Machine Learning, Cloud Engineering, or Generative AI.

Experience Requirements

• Minimum: 4 years | Maximum: 8 years
• Strong hands-on experience in MLOps, DevOps, Cloud Engineering, or ML Platform Engineering.
• Practical experience covering the complete ML production lifecycle: Data and feature pipelines; Model training and experiment management; Model packaging and versioning; Automated deployment; Evaluation and monitoring; Production operations and rollback.
• Hands-on experience deploying and operating ML solutions on AWS.
• Experience implementing CI/CD and Infrastructure as Code for production environments.
• Experience operating containerized workloads using Docker and Kubernetes.
• Experience deploying or operating LLM/GenAI applications in production is highly desirable.
• Experience with RAG, LLM evaluation, prompt/model versioning or Agentic AI systems is an advantage.
• Experience working in enterprise-grade or regulated production environments is preferred.
• Automotive software development experience is an advantage.