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.

• 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

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