Senior QA Engineer - AI Products

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.

• 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.

7+ years of experience in QA / software testing, with 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 Type Script/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:
  Git Hub Actions, Git Lab CI or Jenkins;
  Git.
• Test management and tracking:
  Jira, Test Rail, or Zephyr.
• Excellent written English and clear bug reporting.

• 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.