Worked on the kvcache-ai/sglang repository to deliver Qwen 3.5 model integration, focusing on enhancing model configurability and performance. Updated configuration files and model classes in Python to accommodate new parameters and structures required by the latest model. Implemented adjustments to rope scaling to improve throughput and introduced an expert location configuration method, enabling more precise routing for multi-model deployments. Leveraged skills in machine learning and model configuration to establish a foundation for supporting additional models and accelerating deployment timelines. The work emphasized maintainable code and extensible architecture, addressing evolving requirements in advanced machine learning model support and deployment scenarios.