About the Role
We are looking for a Senior QA Engineer to own quality across our AI products. You will test LLM-powered features end to end, build automated test and evaluation suites, and help ensure every release is accurate, safe, and reliable.
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Responsibilities
- Define test strategies and plans for AI features across web, API, and backend.
- Evaluate model outputs for accuracy, consistency, hallucinations, bias, and safety.
- Build evaluation datasets, including edge cases and adversarial / red-team prompts.
- Develop and maintain automated UI, API, and regression tests integrated into CI/CD.
- Run regression testing on model, prompt, and configuration changes.
- Test performance, latency, error handling, and fallback behavior.
- Report defects clearly and provide release-readiness sign-off.
- Test non-deterministic outputs using scoring rubrics, LLM-as-judge, human evaluation, and defined pass/fail thresholds.
- Perform AI security testing, including prompt injection, jailbreaks, and PII / data leakage across chatbots, RAG, and agents.
- Work with ML engineers and product managers to define quality metrics and acceptance criteria.
- Where applicable, monitor production quality and model drift after release.
Requirements
- 7+ years of experience in QA / software testing, with a strong test automation background.
- Proven experience testing AI/ML or LLM-based products (chatbots, RAG, agents, etc.).
- Hands-on scripting experience in Python and/or TypeScript/JavaScript.
- Hands-on with at least one UI automation framework (Playwright, Cypress, Selenium) and a test framework such as Pytest.
- API testing with Postman, REST Assured, or similar.
- Experience with LLM evaluation tools.
- CI/CD and version control: GitHub Actions, GitLab CI or Jenkins; Git.
- Test management and tracking: Jira, TestRail, or Zephyr.
- Excellent written English and clear bug reporting.
Nice to Have
- Performance/load testing (k6, JMeter, Locust).
- Understanding of prompt engineering, embeddings, and vector databases.
- Experience with AI safety, red-teaming, or content policy testing.
- Cloud experience (AWS, GCP, or Azure) and Docker.
