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AI Testing Engineer
Two disciplines: applying AI tooling to testing work, and testing systems whose output is non-deterministic.
Starting level: Automation Engineer with solid fundamentalsTarget role: Engineer testing AI-powered products and using AI in the workflowJourney: 3–5 months
Core skills
- Prompt design and evaluation for test generation and analysis
- Testing non-deterministic output: rubric scoring, golden sets, tolerance ranges
- Retrieval and grounding checks for hallucination
- Bias, safety and prompt-injection test scenarios
- Cost, latency and token-usage validation
Recommended technologies
- Python or TypeScript
- LLM APIs and SDKs
- Evaluation harnesses
- Vector databases (concepts)
- Playwright/Pytest for orchestration
Practice labs
- Build a golden-set evaluation for a chatbot feature
- Write prompt-injection and refusal test cases
- Compare two model versions on the same rubric
Projects
- An automated evaluation suite reporting accuracy, latency and cost per release
Interview preparation
- How you assert on an answer that changes every run
- Where AI helps and where it should not be trusted in QA
- Designing a regression strategy for a model upgrade
Suggested next step
- AI Quality Lead or ML platform quality roles
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