Definition: An LLM evaluation engineer designs the tests, datasets and metrics that measure whether a large language model application gives accurate, safe and useful results.
Language model output is probabilistic, so ordinary unit tests are not enough. An evaluation engineer builds test sets from real user tasks, defines what a good answer looks like, and scores outputs with a mix of automated checks, model-graded rubrics and human review.
They track accuracy, groundedness, refusal behavior, latency and cost across prompt, model and retrieval changes, and wire these checks into CI so regressions are caught before release. For agents, they also test multi-step tool use and failure handling.
The role becomes important when an LLM feature moves toward production, when the team plans to switch models or providers, or when stakeholders need evidence that the system meets a quality bar before rollout.
Ryz provides senior LLM evaluation engineers from the top 1% of talent, who work on your team, and can include evaluation in an AI pod from the start.
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