Job Description & Details
This is a specialized QA role focused entirely on breaking and validating LLM-driven applications, chatbots, and RAG pipelines where standard deterministic testing simply doesn't apply. If you enjoy moving past traditional software testing to evaluate semantic correctness, hallucinations, and prompt regressions, this is a rare opportunity to tackle cutting-edge generative AI quality challenges.
What You'll Actually Be Doing
Your day-to-day will revolve around designing evaluation frameworks and golden datasets to benchmark non-deterministic model outputs over time. You will spend a lot of time writing Python-based test harnesses, running adversarial and red-team style security assessments on prompts, and validating data pipelines for drift and schema issues. Expect to work closely with data scientists to figure out why a model suddenly decided to hallucinate or drift off-script after a version upgrade.
The Core Tech Stack
You need deep, hands-on experience with Python and AI/LLM evaluation frameworks like Ragas, DeepEval, LangSmith, or Promptfoo, alongside a solid grasp of generative metrics like faithfulness, toxicity, and semantic similarity. The team heavily relies on your ability to write automated test suites that can handle non-deterministic outputs, meaning traditional assertions won't cut it and you'll need to rely on statistical and model-based assertions instead.
Interview Expectations
Expect the interview panel to grill you on how you design evaluation suites for non-deterministic LLM behavior. They will likely ask you how you would test a RAG system for hallucination at scale, and you should be prepared to discuss specific metrics like context precision and answer relevance. The hiring manager wants to see if you can think like an adversary and proactively build red-team strategies before bad prompts reach production.
Application Advice
Make sure your resume leans heavily into your Python scripting background and explicitly highlights any tools like LangSmith, DeepEval, or custom evaluation pipelines you have built. Drop exact keywords from the prompt like 'golden datasets', 'hallucination rate', 'prompt regression', and 'adversarial testing' to ensure you get past the initial ATS filters and land on the engineering team's desk.