Worked on the confident-ai/deepeval repository to deliver integration of the OpenRouterModel into existing metric utilities, expanding the platform’s evaluation capabilities and model flexibility. Focused on enhancing the evaluation pipeline, the work included developing integration tests to verify end-to-end flow and ensure native model recognition, thereby improving test coverage and reliability. Leveraged Python and machine learning expertise to address model interoperability and streamline future deployments. The approach emphasized reproducibility of experiments and reduced friction in model compatibility, contributing to a more robust and adaptable evaluation framework. Testing practices were incorporated to maintain quality and support ongoing development within the project.