We are seeking an experienced Databricks MLOps Developer to build, automate, and manage end-to-end machine learning operations workflows on the Databricks Lakehouse platform. The role involves developing scalable ML pipelines, ensuring seamless model deployment, and enabling monitoring and governance for production-grade ML systems.

Key Responsibilities :

- MLOps Pipeline Development : Build and automate ML training, deployment, and monitoring pipelines using Databricks, MLflow, and Delta Lake.

- Model Lifecycle Management : Manage model versioning and transitions across development, staging, and production environments using MLflow Model Registry.

- CI/CD Implementation : Develop and maintain CI/CD pipelines for ML workloads using Git, Azure DevOps, Jenkins, or GitHub Actions.

- Data Preparation & Feature Engineering : Collaborate with data scientists and data engineers to prepare and optimize feature pipelines on Databricks.

- Monitoring & Governance : Implement model monitoring, data drift detection, and governance using Databricks and cloud-native tools.

- Documentation & Collaboration : Create technical documentation and work closely with cross-functional teams for seamless project delivery.

Required Skills & Qualifications :

- Strong hands-on experience with Databricks, MLflow, and Delta Lake.

- Proficiency in Python and SQL.

- Practical knowledge of ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.

- Experience with CI/CD tools : Azure DevOps, Jenkins, or GitHub Actions.

- Understanding of cloud platforms (AWS / Azure / GCP).

- Knowledge of Docker and Kubernetes for containerization and orchestration.

Preferred Qualifications :

- Databricks certifications (ML Professional / Data Engineer).

- Experience with Unity Catalog for data governance.