Joennlae contributed to both the vllm-project/vllm and ModelCloud/GPTQModel repositories, focusing on stability and scalability in model serving systems. In vllm, Joennlae addressed a concurrency issue in the MQLLM engine by fixing a race condition in Python’s asynchronous code, ensuring reliable loop initialization and correct token ordering under concurrent workloads. For ModelCloud/GPTQModel, Joennlae expanded Gemma 3 model support by integrating larger model variants and updating API compatibility, enabling broader deployment options. The work demonstrated proficiency in Python, API development, and model integration, with careful attention to production reliability and extensibility in high-throughput inference environments.