Adhita Selvaraj focused on backend development and API integration for the databricks-ai-bridge repository, addressing a critical bug in the streaming usage telemetry for the Databricks chat model. Using Python and leveraging expertise in LLM integration, Adhita stabilized the capture and emission of token usage data at the end of streaming responses across multiple APIs. This solution improved the accuracy of usage analytics and cost tracking by ensuring reliable telemetry data. The work involved a clean, low-risk change to the streaming data pipeline, demonstrating a methodical approach to maintaining data integrity and supporting robust monitoring for production machine learning systems.