Lead AI QE & AI Quality Engineer

What the role is, key skills, and how it fits into quality engineering and AI

What is a Lead AI QE or AI Quality Engineer?

A Lead AI QE (Lead AI Quality Engineer) or AI Quality Engineer focuses on ensuring the quality, reliability, and correctness of AI-powered applications. The role combines traditional software quality engineering—test automation, test strategy, CI/CD—with AI-specific work: evaluating model outputs, designing prompts and evals, and using tools like LangSmith to trace and grade LLM behavior. Lead AI QEs often own test strategy for AI features and mentor others on AI quality practices.

Key Skills for an AI Quality Engineer

AI Quality Engineers typically work with test automation (e.g. Cypress, Python), AI evaluation (response quality, correctness, context adherence), and quality engineering practices applied to ML/LLM systems. Familiarity with LangChain, LangSmith, prompt engineering, and metrics like BLEU/ROUGE or custom rubrics is common. A Lead AI QE also needs strong communication and the ability to define test strategy and standards for AI products.

My Experience as an AI QE

I'm Zachary Weston, a Senior Software Quality Engineer with a focus on Lead AI QE and AI Quality Engineer work. I build and run test automation (Cypress, Python), evaluate AI chatbot and LLM outputs with LangSmith and custom grading, and contribute to quality strategy for AI features. You can see my career journey, blog on AI evaluation and quality engineering, and projects (including Cypress + LangSmith for this site), or get in touch.

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