AI Engineer in Test

An AI Engineer in Test (AIET) is a quality engineer who specialises in testing, evaluating and assuring AI-powered applications, including LLM applications, AI agents, RAG systems, machine-learning models and GenAI products.

Unlike traditional software testing, where an input often has a predictable expected output, AI systems can produce non-deterministic and context-dependent responses. An AI Engineer in Test therefore goes beyond conventional pass/fail testing and uses evaluation datasets, metrics, automated evals, statistical testing and human evaluation to determine whether an AI system is good enough and safe enough for production.

The role typically combines:

Traditional Quality Engineering: API, integration, UI, regression, performance and end-to-end testing; automation with Python, Pytest, Playwright or similar tools.
AI/LLM Testing: hallucination, correctness, relevance, groundedness, consistency, toxicity, bias, safety and response-quality evaluation.
RAG Testing: retrieval accuracy, context relevance, faithfulness, citation/source correctness and knowledge-base coverage.
AI Agent Testing: tool selection, tool execution, multi-step reasoning, memory, workflow completion, failure recovery and agent-to-agent interactions.
AI Evaluation Engineering: building golden datasets, test datasets, evaluation rubrics, LLM-as-a-Judge systems and automated evaluation pipelines using frameworks such as DeepEval, Promptfoo and RAGAS.
AI Security & Safety: prompt injection, jailbreaks, data leakage, inappropriate tool use, access-control failures and adversarial testing.
Continuous AI Quality: integrating AI evaluations into CI/CD so model, prompt, RAG, agent or application changes can be regression-tested before production.

An AI Engineer in Test also works closely with AI Engineers, ML Engineers, developers, product teams, security teams and governance teams to define what “good” means for an AI product and establish measurable quality gates.