Infosys—MLOps Engineer

Job description

Description: • 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 Requirements: 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. 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 Skills: 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