Key Responsibilities

End-to-End ML Development: Design, build, and deploy scalable machine learning solutions, NLP applications, and Generative AI (GenAI) frameworks.

Pipeline Engineering: Develop and manage production-grade ML pipelines using Databricks, Apache Spark, and MLflow for seamless model tracking and deployment.

Self-Service Analytics: Configure and optimize Databricks Genie to democratize data insights and enable automated, natural-language data discovery across teams.

Workflow Optimization: Maintain, monitor, and continuously improve existing production ML workflows, ensuring high availability, speed, and reliability.

• Collaboration: Work closely with data scientists, data engineers, and business stakeholders to translate complex requirements into robust data products.