Description

  • Design and deploy predictive models (e.g., forecasting, churn analysis, fraud detection) using Python/SQL, Spark MLlib, and Databricks ML
  • Build end-to-end ML pipelines (data ingestion → feature engineering → model training → deployment) on Databricks Lakehouse
  • Optimize model performance via hyperparameter tuning, AutoML, and MLflow tracking
  • Collaborate with engineering teams to operationalize models (batch/real-time) using Databricks Jobs or REST APIs
  • Implement Delta Lake for scalable, ACID-compliant data workflows. 
  • Enable CI/CD for ML pipelines using Databricks Repos and GitHub Actions
  • Troubleshoot issues in Spark Jobs and Databricks Environment

Key Skills
Education

Any Gradute

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