A newer loop with less settled conventions. Expect evaluation to dominate — how you measure whether a non-deterministic system is working — alongside retrieval design, prompt strategy and cost per request.
Also advertised as AI Engineer, LLM Engineer, GenAI Engineer — the loop is the same, and so is the practice below.
Data loops test two things that pull against each other: rigour with methods, and the judgement to know when a rough answer is the right one. Candidates who are strong on one are often visibly weak on the other.
Expect questions where the data is deliberately insufficient. Stating the assumption you are making, and what would change your answer, is most of the mark.
MockLive AI runs a spoken AI / LLM Engineer mock interview — behavioral, live coding, system design and technical deep-dive rounds — with an AI interviewer who asks questions like “How do you evaluate a system whose output is different every time?”, follows up on what you actually say and scores you on a fixed rubric; the first 7-minute interview is free. How it compares with other AI interview tools →
Each round is run by the interviewer built for it, with its own scoring axes — not one generic interviewer asked to change subject.
A balanced round across experience and role-specific topics.
Scored on: Analytical communication · Data & inference rigor · Decision quality
Past experience, STAR stories, collaboration, and conflict.
Scored on: Analytical communication · Data & inference rigor · Decision quality
Solve a coding problem in the editor, out loud.
Scored on: Problem-solving communication · Engineering correctness · Adaptability & craft
How the live coding round works →Architecture, scale, data models, and trade-offs.
Scored on: Technical communication · Engineering depth · Ownership & collaboration
Drill into your resume projects and domain fundamentals.
Scored on: Analytical communication · Data & inference rigor · Decision quality
Six you can expect, in the register interviewers actually use. Answer them out loud before you read the next section — reading a question and answering one are different skills, and only the second is marked.
The gap between these two is most of your score, and it is easier to see than to be told.
“How do you evaluate a system whose output is different every time?”
"We check the outputs look good." Manual spot-checking does not scale past a handful of cases and cannot catch a regression.
A held-out set with graded cases, a scoring method you can defend — deterministic checks where possible, model-graded where not, human-labelled on a sample — run on every prompt or model change. Evaluation is the discipline of this role and the loop is mostly about it.
The post-mortem nobody sends you. These are specific to this loop rather than general interview advice.
For AI / LLM Engineer roles, a general round runs with Marcus — metrics, experiments, causal reasoning, and decisions under uncertainty. It is the fastest way to find out which round you actually need to work on. The first session is free.
Practise an AI / LLM Engineer interview →Before you practise, it is worth reading how the common rounds are marked: tell me about yourself, behavioural questions and STAR, and system design.
Questions like these: “How do you evaluate a system whose output is different every time?” “Walk me through your retrieval design and where it fails.” “How do you keep cost per request under control as usage grows?” “What is your fallback when the model returns something unusable?” “How do you stop a prompt change from silently regressing something else?” “When would you fine-tune rather than change the prompt or the context?”
Expect General / Mixed (A balanced round across experience and role-specific topics), Behavioral (Past experience, STAR stories, collaboration, and conflict), Live Coding (Solve a coding problem in the editor, out loud), System Design (Architecture, scale, data models, and trade-offs) and Technical Deep-Dive (Drill into your resume projects and domain fundamentals). A newer loop with less settled conventions. Expect evaluation to dominate — how you measure whether a non-deterministic system is working — alongside retrieval design, prompt strategy and cost per request.
No evaluation story. It is the single most common reason candidates fail this loop. Prompt engineering as the entire answer, with nothing on retrieval quality, latency or cost. No plan for the model being confidently wrong in front of a user.
Out loud, with someone who follows up. MockLive AI's interviewer for this role asks these questions, pushes back on thin answers and scores you on a fixed rubric, with the transcript and every answer replayable. The first 7-minute interview is free and needs no card.