QA Automation & AI Engineering
I come from QA automation and now focus on testing and securing LLM/AI systems: evaluation, red-teaming, RAG safety and self-healing E2E tests. I like test suites that run offline, gate merges in CI, and are honest about what they don't cover yet.
- ai-qa-factory: local, human-in-the-loop workspace that generates Playwright tests, diagnoses failures and proposes locator repairs, which are applied only after explicit approval. Python, Streamlit, Playwright/TypeScript, OpenRouter; CI on Python 3.10–3.12.
- rag-security-sandbox: RAG app and pytest fuzzer for prompt injection, data poisoning and prompt leaking, with regex + LLM-as-a-judge guardrails. FastAPI, ChromaDB, Groq. Offline test suite in CI; known security gaps are documented as
xfail(strict=True)tests. - llm-api-benchmark: pytest framework benchmarking local LLMs (via Ollama) on latency, instruction following, LLM-as-a-judge, red-team prompts and RAG hallucinations. Test data in JSON, HTML reports, offline suite in CI.
- playwright-python-e2e-framework: BDD E2E suite (pytest-bdd, Page Object Model) with a credential-free demo suite gating CI and a manual live-email suite. Python, Playwright, GitHub Actions.
- triglav-harness: experimental public preview of a local Python coding controller.
- repo-guard: fast local git guardrail against accidental secret leaks in AI-generated code. Python.
- prompt-refinery: Architect → Referee pipeline that turns raw tasks into contract-validated prompts for AI IDE agents, with an MCP server, Streamlit GUI and 258 offline tests. Python.
I build with several AI coding agents – Claude Code as the lead, plus Codex and Mistral (Vibe CLI / OpenCode), in Zed and Antigravity. A human stays in the loop:
- agents work on branches, never directly on
main - git hooks block pushes and changes to
main - every change goes through a reviewed pull request with green CI
Also: pytest-bdd, ChromaDB, LLM APIs (OpenRouter, Groq).

