Description RESPONSIBILITIES:
ML Model Deployment & Platform Management
• Lead the design, implementation, and ongoing maintenance of scalable ML infrastructure on Databricks, including ML flow for experiment tracking, model registry, and model serving endpoints.
• Oversee the development of the ML Ops platform and automated pipelines for deploying, monitoring, and maintaining models within production environments.
• Implement robust solutions for model versioning, systematic retraining, and comprehensive artifact management using Databricks Unity Catalog for ML governance.
• Design and manage Databricks Feature Store for consistent feature engineering across training and inference pipelines.
Generative AI & LLM Operations
• Architect and implement Retrieval-Augmented Generation (RAG) systems for document Q& A, enabling business teams to query fund documents, investor letters, and market research.
• Design, deploy, and manage vector database solutions (Databrick...