QA Tester for Secure AI Knowledge MVP

Title: QA Tester for AI Q&A and Document Metadata MVP (RAG / LLM testing)

About the project We are building an AI MVP for a UK government-funded knowledge platform on climate, environment and nature. It has two features: a question-and-answer assistant that gives cited answers from a curated document library, external libraries and the web; and an automated metadata extractor for bulk document uploads, reviewed by a communications team before publishing. Security and accuracy are critical.

What you will do
• Write a test strategy and plan
• Build a golden set of test questions and expected answers, and score answer accuracy, citations and hallucinations
• Test bulk upload and metadata extraction accuracy
• Test for prompt injection, data leakage and access-control issues
• Run regression, performance, accessibility (WCAG 2.2 AA) and cross-browser checks
• Log clear defects and send a short weekly QA report
• Produce the MVP acceptance report and support a small user usefulness test

Must have
• 3+ years of QA on web applications
• Proven experience testing LLM or RAG applications
• Knowledge of AI security risks (OWASP Top 10 for LLMs)
• Strong written English

Nice to have
• Promptfoo, Ragas, DeepEval or similar; Playwright or Cypress
• Accessibility testing
• Public-sector or donor-funded project experience

Engagement
• Remote, part-time (about 20 hours per week)
• Mid-November 2026 to end of January 2027, starting with a 2-week trial
• At least 2 hours' daily overlap with GMT
• NDA required
• Budget:

To apply, answer these:
1. Describe one LLM or RAG application you tested. How did you measure answer quality?
2. How would you test whether a cited answer is actually supported by its source?
3. Give two prompt injection tests you would run against a document Q&A tool.
4. Which tools would you use for this project, and why?
5. Your availability from mid-November and your rate.

Applications without answers to these questions will not be considered.