Description :

- Manage and optimize Databricks platform (Workspaces, Jobs, Unity Catalog, Delta Lake)

- Implement the whole ML lifecycle : model training, versioning, deployment, monitoring, and retraining

- Track, manage, and govern ML experiments & models via MLflow

- Develop scalable data/ML pipelines with Python (pandas, scikit-learn, PyTorch/TensorFlow), PySpark & SQL

- Deploy and manage solutions on AWS (specifically Sagemaker); knowledge of Docker/Kubernetes required

- Design and drive deployment strategies (A/B testing, blue-green & canary deployments)

- Create CI/CD workflows for ML using Jenkins/GitHub Actions/GitLab CI

- Monitor data quality, performance, and drift using Databricks Lakehouse Monitoring; integrate SHAP/LIME for explainability

- Automate end-to-end processes : data validation, feature generation, model building & deployment

- Collaborate across Data Science, Engineering, DevOps, and Business teams

- Mentor juniors, create clear documentation, and contribute to standard operating procedures

Mandatory Skills :

- Databricks (core)

- MLflow, End-to-end MLOps & ML lifecycle

- Python, PySpark, AWS Sagemaker, Docker/Kubernetes, CI/CD (Jenkins, GitHub Actions, GitLab CI)

Requirements :

- 4 to 6 years experience in MLOps, Data Engineering, or AI/ML roles

- Strong background in building, deploying & maintaining ML models at scale in the cloud

Location : Pune, Bangalore, Noida, Gurgaon

Looking for Immediate Joiners