MLOps Engineer

We are seeking an experienced MLOps Engineer with strong expertise in deploying, managing, and optimizing machine learning workloads in production environments. This role is primarily focused on MLOps (60%), supported by AWS Cloud (25%) and DevOps (15%) capabilities. The ideal candidate will have hands-on ownership of the end-to-end ML lifecycle, including model training, deployment, monitoring, automation, performance optimization, and retraining. Strong experience with AWS services, CI/CD pipelines, containerization, infrastructure automation, and production-grade ML platforms is essential. This is not a generic DevOps role; candidates must demonstrate proven experience in operationalizing and maintaining ML models at scale in cloud environments. Technical Requirements: 1. SageMaker 2. MLflow 3. Kubeflow 4. Databricks 5. MLOps 6. Model Deployment 7. Model Monitoring 8. Model Retraining 9. Feature Store 10. CI/CD for ML 11. Training Pipelines 12. Inference Pipelines 13. Drift Detection 14. Docker 15. Kubernetes/EKS 16. Terraform 17. CloudFormation 18. AWS Lambda 19. Python Automation Responsibilities: • 5+ yrs DevOps/Cloud/MLOps experience; Python for scripting and automation • Strong with Jenkins, Git, Docker, EKS troubleshooting • AWS: SageMaker, Lambda, S3, ECS, IAM, RDS, infra creation • IaC: CloudFormation (CFT) and Terraform • MLOps: build/operate ML pipelines deploying to SageMaker, Databricks, or Lambda Preferred Skills: Technology->AI-Data science->PYTHON,Technology->AI-Physical AI-IOT->IOT Analytics - Machine Learning