Worked on the neuralmagic/guidellm repository to enhance the reliability of its task scheduling subsystem by addressing a concurrency control issue. Replaced a busy-wait loop with an asyncio.Semaphore, leveraging Python’s asynchronous programming capabilities to reduce unnecessary CPU usage and improve the timing of task completions. This change resolved a bug where the previous approach could lead to mis-timed completions and scalability challenges under concurrent workloads. Focused on concurrency control and asynchronous programming, the update improved both efficiency and reliability in the scheduling logic. The work demonstrates a targeted, in-depth approach to optimizing Python-based systems for concurrent task management.