Contributed to the vllm-project/vllm and ModelCloud/GPTQModel repositories by addressing both reliability and scalability challenges in Python-based machine learning systems. Improved the stability of the MQLLM engine by resolving a race condition in asynchronous code, ensuring correct token ordering and robust loop initialization under concurrent workloads. Later, expanded the ModelCloud/GPTQModel API to support larger Gemma 3 model sizes, integrating new model classes and updating deployment mappings to enhance scalability and throughput. Demonstrated expertise in Python, asynchronous programming, and API development, with a focus on production reliability, model integration, and meeting evolving requirements for large-scale inference tasks.