Worked on the google-ai-edge/LiteRT-LM and ai-edge-torch repositories to deliver six features and one bug fix focused on performance, deployment, and interoperability for AI models. Developed configurable benchmarking tools across Python and Swift bindings, enabling more precise performance evaluation. Added LoRA support and model conversion utilities to streamline deployment from Hugging Face to LiteRT TFLite formats. Enhanced backend acceleration for audio models on NPU and GPU, modernized APIs for improved chat management, and expanded test coverage for CPU environments. Addressed a regression in NPU Int16 embedding scaling, demonstrating depth in C++, Python, and machine learning model optimization and testing.