Contributed to backend development and maintainability improvements in Python-based machine learning infrastructure, focusing on the HabanaAI/vllm-fork and vllm-project/vllm-ascend repositories. Addressed code clarity by correcting a variable naming typo in the TPUWorker class, which improved readability and reduced onboarding friction for TPU-related workflows. Later, refactored distributed logic in vllm-ascend by consolidating duplicated methods and simplifying broadcast paths, ensuring compatibility across vLLM versions without altering external behavior. Applied object-oriented programming and software design patterns to reduce maintenance overhead and future regression risk, demonstrating a methodical approach to code quality, debugging, and long-term codebase evolution in distributed Python systems.