Worked on the databricks-ai-bridge repository to deliver the Genie API Execution Traceability Enhancement, focusing on improving observability and debugging efficiency for Genie API executions within MLflow pipelines. The approach involved increasing the polling frequency and introducing granular spans to capture each status change during Genie API execution, thereby enhancing traceability in distributed systems. Unit tests were implemented to validate the improved traceability, ensuring reliability in production-like environments. The work leveraged Python, MLflow instrumentation, and distributed tracing concepts, resulting in more transparent execution flows and enabling faster issue diagnosis for teams integrating Genie API with MLflow-based workflows.