Over a two-month period, contributed targeted backend enhancements to the mozilla/onnxruntime and intel/onnxruntime repositories, focusing on deep learning model compatibility and execution provider robustness. Addressed a blocking assertion in C++ that previously prevented MaxPool operations with auto_pad set to 'VALID' on the QNN backend, enabling support for zero-padding configurations and improving interoperability. Later, implemented a ConvTranspose output shape padding enhancement for the QNN Execution Provider, ensuring correct padding calculation when output_shape is specified, in line with ONNX specifications. Work emphasized algorithm design, backend development, and unit testing, resulting in improved accuracy and flexibility for on-device inference workloads.