Worked on backend reliability and optimization for the AI-Hypercomputer/maxtext and AI-Hypercomputer/xpk repositories, focusing on critical bug fixes in Python. Addressed model packaging and conversion issues by refining parameter mapping for Gemma3 checkpoint conversion and mitigating out-of-memory errors through memory-aware configuration of JAX and TensorFlow. Enhanced the stability of multimodal deployment workflows, enabling smoother CI/CD and reducing deployment risk. In the xpk repository, improved DWS Calendar reservation matching by correcting accelerator type identification and aggregate matching logic, which reduced resource misallocation. Demonstrated depth in data processing, machine learning, and backend development, prioritizing operational efficiency and robust deployment pipelines.