
Worked on optimizing AI model deployment and workflow automation across the microsoft/onnxruntime, microsoft/Olive, and olive-recipes repositories. Developed and integrated CI/CD pipeline upgrades using Azure Pipelines and Python, enabling compatibility with the latest QNN SDK and improving hardware-accelerated inference for Qualcomm NPUs. Implemented ONNX model transformation passes and end-to-end workflows for generative AI, focusing on quantization, profiling, and device-specific optimization. Addressed quantization accuracy and test reliability by refining per-channel quantization logic and stabilizing CI tests in C++ and Python. Enhanced deployment efficiency and model performance, while maintaining robust testing and documentation standards throughout the development process.
April 2026 monthly summary focusing on key accomplishments, business impact, and technical achievements for Microsoft Olive and Olive-Recipes.
April 2026 monthly summary focusing on key accomplishments, business impact, and technical achievements for Microsoft Olive and Olive-Recipes.
September 2025: Stability and quantization improvements for microsoft/onnxruntime. Key deliverables include stabilizing ONNX attention tests by relaxing tolerances to reduce CI false negatives, and fixing per-channel quantization in QNN models (removing unnecessary workarounds and correcting uint symmetric zero-points). Impact: improved CI reliability, faster iteration cycles, and more accurate quantization for production deployments. Technologies demonstrated include ONNX Runtime, QNN quantization, test tolerances, and CI automation.
September 2025: Stability and quantization improvements for microsoft/onnxruntime. Key deliverables include stabilizing ONNX attention tests by relaxing tolerances to reduce CI false negatives, and fixing per-channel quantization in QNN models (removing unnecessary workarounds and correcting uint symmetric zero-points). Impact: improved CI reliability, faster iteration cycles, and more accurate quantization for production deployments. Technologies demonstrated include ONNX Runtime, QNN quantization, test tolerances, and CI automation.
August 2025 Monthly Summary (microsoft/onnxruntime and microsoft/Olive) Key features delivered: - CI/CD Pipeline: Upgraded QNN SDK to v2.37.0 in Azure pipelines for microsoft/onnxruntime to unlock compatibility with latest features and improvements; commit f8c6262399e2c7e0a58cd494f0e58d4f4262dc43. - QAIRT MHA2SHA transformation pass: Implemented in Olive to optimize ONNX model splits for Qualcomm NPUs; includes Python implementation files and comprehensive unit tests; commit 6457911511dcadfdd5f1e0cd5757571ddfd32419. Major bugs fixed: - No major bugs reported in the provided scope for August 2025. Overall impact and accomplishments: - Strengthened cross-repo collaboration and readiness for hardware-accelerated inference on Qualcomm NPUs; reduced deployment friction by keeping tooling up-to-date; improved potential performance through model-split optimization. Technologies/skills demonstrated: - Azure DevOps CI/CD, QNN SDK integration, Olive framework enhancements, QAIRT modernization, Python development, unit testing, ONNX optimization, NPU-focused performance considerations. Business value: - Accelerated release cycles with up-to-date SDKs, improved runtime efficiency on target NPUs, and decreased risk from outdated tooling.
August 2025 Monthly Summary (microsoft/onnxruntime and microsoft/Olive) Key features delivered: - CI/CD Pipeline: Upgraded QNN SDK to v2.37.0 in Azure pipelines for microsoft/onnxruntime to unlock compatibility with latest features and improvements; commit f8c6262399e2c7e0a58cd494f0e58d4f4262dc43. - QAIRT MHA2SHA transformation pass: Implemented in Olive to optimize ONNX model splits for Qualcomm NPUs; includes Python implementation files and comprehensive unit tests; commit 6457911511dcadfdd5f1e0cd5757571ddfd32419. Major bugs fixed: - No major bugs reported in the provided scope for August 2025. Overall impact and accomplishments: - Strengthened cross-repo collaboration and readiness for hardware-accelerated inference on Qualcomm NPUs; reduced deployment friction by keeping tooling up-to-date; improved potential performance through model-split optimization. Technologies/skills demonstrated: - Azure DevOps CI/CD, QNN SDK integration, Olive framework enhancements, QAIRT modernization, Python development, unit testing, ONNX optimization, NPU-focused performance considerations. Business value: - Accelerated release cycles with up-to-date SDKs, improved runtime efficiency on target NPUs, and decreased risk from outdated tooling.

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