- Build and manage end-to-end MLOps pipelines for ML model development and deployment.
- Develop data/ML workflows using Python and PySpark.
- Implement CI/CD pipelines for automated model testing, deployment and monitoring.
- Containerize ML applications using Docker and manage orchestration using Kubernetes or equivalent platforms.
- Work with AWS/Azure/GCP cloud services for scalable ML infrastructure.
- Implement model deployment, versioning, monitoring and automation.
- Collaborate with Data Scientists, Data Engineers and DevOps teams to productionize ML models.
- 4 years of experience in MLOps / ML Engineering / Data Engineering.
- Strong hands-on experience with Python, PySpark, CI/CD and Git.
- Practical experience in Docker/containerization and Kubernetes/orchestration.
- Experience working on at least one major cloud platform: AWS, Azure or GCP.
- Robust understanding of ML lifecycle, automation and production deployment.