Jiming Ruan developed and integrated RMSNorm functionality into the ROCm/aiter library, expanding its normalization options for machine learning workflows. He updated the Python package to expose RMSNorm operations by modifying the __init__.py file and adjusted the build process to correctly map CUDA bindings, specifically targeting rmsnorm_pybind.cu and rmsnorm_kernels.cu. This work, implemented using C++ and Python, ensured that RMSNorm could be seamlessly accessed within Aiter, simplifying downstream model integration and improving training stability. The feature was validated for production readiness, reflecting a focused and technically sound contribution that addressed both API exposure and runtime integration challenges.