Key Responsibilities 1 Machine Learning Development Design develop and optimize ML models for predictive analytics classification regression NLP or other use cases Perform exploratory data analysis EDA feature engineering data preprocessing model selection tuning and evaluation Implement responsible AI practices including model performance monitoring drift detection and interpretability 2 Databricks Platform Expertise Develop and maintain Databricks notebooks jobs Delta Lake pipelines and MLflow tracking workflows Optimize large-scale data workloads using Spark Delta Live Tables and Databricks clusters Manage data access lineage and governance through Databricks Unity Catalog 3 MLOps Productionization Build and maintain end-to-end ML pipelines using Databricks MLflow and CI CD tools Azure DevOps GitHub Actions Jenkins Deploy models to production using MLflow Models Databricks Model Serving or containerized microservices Implement automated monitoring for model drift data quality and inference performance Support continuous model retraining strategies and versioning of datasets features and models 4 Data Engineering Collaboration Work closely with Data Engineering to design scalable ETL ELT pipelines on Delta Lake Ensure high availability of feature pipelines and support maintenance via the feature store Databricks Feature Store 5 API Integrations Develop RESTful APIs for real-time model inference and analytics workflows Integrate with internal and external systems using API gateways event-driven architectures or message queues Ensure security observability and performance of deployed endpoints 6 Governance Security Compliance Apply data governance best practices across Unity Catalog including permissions lineage tracking and data auditing Comply with enterprise security controls secrets management and model governance frameworks Required Skills Experience 3-8 years of experience in Data Science ML Engineering adjust as needed Solid hands-on experience with Databricks Spark Delta Lake and MLflow Proficiency in Python SQL and common ML libraries scikit-learn PySpark MLlib TensorFlow PyTorch optional Solid understanding of MLOps concepts CI CD feature stores monitoring model deployment pipelines Experience integrating ML systems via REST APIs or event-driven services Deep understanding of ML lifecycle data ingestion training evaluation deployment monitoring Familiarity with cloud platforms Azure AWS or GCP preferably Azure Databricks Experience with Unity Catalog data governance and access control

📌 Ml Engineer + Data Scientist (Karnataka)
🏢 VEGA Intellisoft Private
📍 Karnataka