Developed Mixture-of-Experts (MoE) model inference support for Sarvam large language models within the sgl-project/sglang repository, focusing on scalable and efficient AI workloads. The work involved designing and implementing new configurations and MoE-handling classes to extend the platform’s model coverage, enabling enterprise-ready deployment of Sarvam MoE architectures. Leveraging deep learning and natural language processing expertise with PyTorch and Python, the developer ensured robust integration and maintainable code. Collaboration with cross-functional teams and attention to architectural design allowed for seamless expansion of the library’s capabilities, addressing the need for efficient inference in large-scale machine learning applications without introducing major bugs.