MLOps Engineer

Job DescriptionWe 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.
Roles 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
Technical Requirement1. 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
Educational RequirementMCA,MSc,MTech,Bachelor of Engineering,BTech
Preferred SkillsTechnology->AI-AI Engineering->MLOps,Technology->AI-Data science->Machine Learning,Technology->Devops->Ansible,Technology->OpenSystem->Python - OpenSystem
Service LineData Analytics Unit