Senior QA Engineer · AI-Assisted Test Systems · OTT/Media · Fintech · E-commerce
12 years designing quality systems where AI-assisted reasoning works alongside
deterministic validation — not as a replacement for it.
| Area | Detail |
|---|---|
| Agentic QA systems | LLM-assisted reasoning with confidence scoring, deterministic guardrails, and human-in-the-loop checkpoints |
| Vision-based automation | UI understanding from screenshots, multimodal test exploration, visual fallback strategies |
| Explainable test repair | Failure analysis pipelines that produce auditable decision artifacts — when something breaks or gets fixed, there's a clear record of why |
| OTT/Media quality | Playback validation, DRM compliance, cross-device certification across 8+ platforms |
| CI/CD quality gates | Classical automation integrated with AI-assisted validation signals, without turning the pipeline into a black box |
| Project | Stack | Description |
|---|---|---|
| test-playwright-protocol | TypeScript, Playwright | Smart Playwright Protocol (SPP) — a lightweight, protocol-driven workflow for AI-assisted Playwright automation using Markdown tasks, verification gates, and Page Object best practices. |
→ More on the portfolio site
Performance · Databases · Devices
I'm skeptical of QA systems that treat model output as ground truth. My preference is architectures where AI handles the fuzzy work — pattern recognition, failure summarization, test suggestion — while deterministic checks enforce the invariants that actually matter. Every automated decision should be auditable. Claims about AI-assisted testing should be defensible in production, not just in demos.
I write about this on Medium · Models and experiments on Hugging Face


