Worked on the Azure/azure-sdk-for-python repository to deliver performance and reliability enhancements for the IntentResolution evaluator. Focused on improving speed, reducing intra- and inter-model variance, and lowering token usage to optimize cost efficiency. The approach involved implementing robust unit tests, addressing corner cases, and integrating a logger to enhance observability and maintainability. Updated documentation and changelogs ensured clarity for future development and production rollout. Leveraged Python and AI/ML expertise, particularly in LLM evaluation, to achieve measurable quality gains. The work enabled more reliable and scalable deployment of the evaluator, supporting efficient production use and streamlined ongoing maintenance.