Kingston, Ontario, Canada
Impact: Shipped production-leaning automation that cut display/backlight regression time by ~80%, while seeding AI workflows and analytics that improved delivery visibility and engineering throughput. What I built & drove Android automation (Python + UIAutomator2): Built a resilient brightness/backlight/AOD self-check suite with multi-path control (Status Bar, SystemUI, Settings). Avoided hard-coded IDs/XPaths via heuristic discovery; added a CustomTkinter GUI and macro-config checks (YAML, relative paths) for easy reuse across teams. Log & diagnostics: Authored an ADB logcat grabber (tag filters, timed capture, trigger commands) that finally reproduced “CLI sees it, tools don’t” cases—then surfaced terminal-level insights in the GUI. Version self-tests: Designed a device-framework self-check tool covering backlight, AOD, and power-management regressions; abstracted control IDs/command templates into configs for maintainability; verified on MTK and adapted for Unisoc. AI enablement Helped evangelize AI2D Project / Trae IDE: talk track, teammate interviews, and SOPs for code gen, log triage, and CRUD tasks; explored MCP/Agent patterns (local convo history, code review, log queries). Documented a hands-on AI-assisted coding SOP for day-to-day development. Docs & knowledge Wrote design/usage guides, troubleshooting notes, and a polished internship report; standardized configs and packaging so non-dev users can run tests without touching code. Stack: Python, UIAutomator2, ADB/logcat, CustomTkinter, YAML, pandas; Android display/AOD/power flows; MTK & Unisoc. Result: Faster regressions, clearer delivery metrics, and higher AI adoption—plus a reusable toolkit teammates actually enjoy using.