An AI evals engineer designs the tests that tell a team whether an AI system is good enough: the datasets, the scoring (code checks, human review, or a model as a judge), and the process that runs them on every change. It is a real and growing specialism inside AI engineering, and a strong angle for juniors because few people do it well.
What people tell me
I keep seeing "evals" mentioned in AI engineering job posts and some companies now hire for AI evaluation roles specifically. I have built a few LLM projects, but I never really tested them beyond trying some prompts. What does an AI evals engineer actually do day to day, and is it a good direction for someone early in their career?
A composite of the messages behind this question, with personal details left out.
Key takeaways
- Evals answer one question: is this AI system good enough for its job, and did the last change make it better or worse?
- The work is building test sets, choosing how to score outputs, and running evals automatically on changes.
- Scoring mixes code checks, human review and LLM-as-judge, each with known weaknesses.
- Strong evals skills make you better at any AI engineering role, not only evals-titled ones.
- The best proof is a small LLM project with a written eval suite and before-and-after results.
Why evals became a job
Normal software either passes a test or it does not. LLM outputs vary, can be partly right, and can get worse after a small prompt or model change without anyone noticing. Teams shipping AI features need a reliable way to measure quality, catch regressions and decide when something is ready. That is what evaluation, or "evals", means, and in many teams it has become a focused role.
What the work involves
| Task | What it looks like |
|---|---|
| Defining "good" | Turning a vague goal ("helpful answers") into criteria someone can check |
| Building test sets | Collecting realistic inputs, edge cases and known failures, with expected behaviour |
| Choosing scorers | Exact checks in code, human ratings, or an LLM as a judge with a clear rubric |
| Checking the judges | Making sure automated scores agree with careful human ratings |
| Running evals in CI | Every prompt, model or retrieval change runs the suite before it ships |
| Monitoring production | Sampling real traffic, labelling failures, feeding them back into the test set |
| Reporting | Short, honest summaries that help the team decide |
Skills you need
- Software engineering basics: Python, testing, data handling, CI.
- How LLM apps work: prompts, retrieval, tool calling, agents. The AI engineering roadmap covers this.
- Measurement thinking: sampling, inter-rater agreement, avoiding overfitting to your own test set.
- Writing: clear rubrics and clear reports.
- Tools: open-source frameworks such as promptfoo and DeepEval, and tracing tools such as Langfuse. Tools change; the thinking does not.
The AI evals cheat sheet summarises methods and pitfalls, and AI evals: everything you need to know is a good longer read.
Is it a good direction for a junior?
Yes, as a skill, and sometimes as a title. Dedicated evals roles are still fewer than general AI engineering roles, and many sit in AI labs, larger product teams and data or annotation companies. But evals skills make you stand out in any AI engineering interview, because most candidates cannot say how they know their system works.
How to prove it
- Take an LLM project you already built, such as a RAG app.
- Write 50 to 100 realistic test cases, including tricky ones.
- Score them with a mix of code checks and an LLM judge, and compare the judge with your own ratings on a sample.
- Change one thing (prompt, chunking, model) and show the before-and-after results.
- Write it up in the README.
How do I prove an AI agent project works? goes deeper on this.
Common mistakes in evals work
- Testing only easy cases. A test set full of happy paths always passes.
- Trusting an LLM judge blindly. Judges have biases, such as preferring longer answers. Check them against human ratings.
- Changing the test set every time. You lose the ability to compare versions. Keep a stable core set and add new cases carefully.
- Reporting one average number. Break results down by type of input; averages hide the failures that matter.
Where these roles appear
Look for titles such as AI evaluation engineer, model evaluation, AI quality, or applied AI engineer with evaluation in the description. Many general AI engineering posts also list evaluation as a core duty, which is a good sign the skill is valued.
If you are still stuck
Evals overlap with data annotation and AI trainer work; Is data annotation a real career? explains how. For portfolio projects, see prompt change regression evals in pull requests and the sentence-level groundedness checker. If you want help planning your move into AI engineering, join Sefism and, as a member, book a 1:1 session.
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Read next
- An AI Engineering Roadmap That Puts Things in the Right OrderAn AI engineering roadmap in seven stages, from tokens and prompting through RAG, evals, tool calling, MCP and agents, with a project to build at each step, what to skip, and a realistic timeline.
- The AI Engineering Words You Keep Hearing, Explained ProperlyAI engineering terms in plain language: tokens, context windows, embeddings, RAG, fine tuning, tool calling, MCP, agents, evals and more, with what each is good for, where it lets you down, and the order to learn them.
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