Portfolio

Professional case studies.

Four examples of turning difficult backend, computer-vision, and engineering-effectiveness problems into practical results.

backendapplied AIautomationreliabilityleadership

Applied AI · Automation

A Python calibration platform that turned a complex, engineer-led computer-vision setup into a repeatable workflow.

Problem: Every new drive-through store required engineers to interpret camera tracks, place markers, and tune lane geometry by hand. The process was slow, fragile across store layouts, and pulled senior engineers into recurring setup work.
Outcome: Reduced initial store setup effort by 50%, from 40 to 20 engineer-hours per store, while creating a maintainable foundation for handling new layouts and calibration edge cases.

PythonOpenCVYOLOscikit-learn

Computer Vision · Reliability

Detector and tracking improvements for a multi-camera drive-through platform operating on noisy real-world video.

Problem: Missed detections disrupted vehicle histories across entry, merge, and exit zones. The resulting gaps reduced the accuracy of downstream timing and analytics and were difficult to reproduce consistently.
Outcome: Reduced missed vehicle detections by approximately 77%, from 22 to 5 per 100 vehicles, improving the stability of downstream tracking and operational metrics.

PythonYOLOv8/11OpenCVPyTorch

Engineering Effectiveness

A unified pytest-based test strategy that made a legacy computer-vision codebase safer to change.

Problem: The core service had fragmented test structures, limited automation, and only 10% coverage. Engineers had little confidence that changes to tracking, calibration, or configuration behavior would remain isolated.
Outcome: Increased automated test coverage from 10% to 34% and created a stronger release signal for changes across the core Python platform.

PythonpytestJenkinsDocker

Technical Leadership

A coordinated refactor that removed duplicated logic and separated proprietary algorithms from the product shell.

Problem: Core behavior had grown across duplicated paths, making changes risky and obscuring the boundary around proprietary components needed for a patent application.
Outcome: Improved maintainability and modularity of the core Python module while producing a cleaner technical boundary for patent-related work.

PythonArchitectureCode ReviewMentoring