EXCEEDS logo
Exceeds
qti-kromero

PROFILE

Qti-kromero

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.

Overall Statistics

Feature vs Bugs

67%Features

Repository Contributions

6Total
Bugs
2
Commits
6
Features
4
Lines of code
8,680
Activity Months3

Work History

April 2026

2 Commits • 2 Features

Apr 1, 2026

April 2026 monthly summary focusing on key accomplishments, business impact, and technical achievements for Microsoft Olive and Olive-Recipes.

September 2025

2 Commits

Sep 1, 2025

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

2 Commits • 2 Features

Aug 1, 2025

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.

Activity

Loading activity data...

Quality Metrics

Correctness93.4%
Maintainability86.6%
Architecture93.4%
Performance86.6%
AI Usage36.6%

Skills & Technologies

Programming Languages

C++JSONCPythonYAML

Technical Skills

AI DevelopmentAzure PipelinesC++ developmentCI/CDContinuous IntegrationDevOpsLoggingMachine LearningModel OptimizationONNXPass DevelopmentProfilingPython ProgrammingPython developmentQuantization

Repositories Contributed To

3 repos

Overview of all repositories you've contributed to across your timeline

microsoft/onnxruntime

Aug 2025 Sep 2025
2 Months active

Languages Used

YAMLC++JSONCPython

Technical Skills

Azure PipelinesContinuous IntegrationDevOpsC++ developmentCI/CDPython development

microsoft/Olive

Aug 2025 Apr 2026
2 Months active

Languages Used

Python

Technical Skills

Model OptimizationONNXPass DevelopmentTestingAI DevelopmentMachine Learning

microsoft/olive-recipes

Apr 2026 Apr 2026
1 Month active

Languages Used

Python

Technical Skills

LoggingMachine LearningModel OptimizationProfilingQuantization