Every question below is one that gets asked. What we have added is the part that usually goes unsaid: what a strong answer actually demonstrates. Career1 scores interviews for a living, so this is the difference we see between an answer that lands and one that was rehearsed.
Building with models
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Walk me through something you shipped that used a model in production.
What a strong answer shows: Scope and ownership. The follow-ups are always about what went wrong, which is where borrowed projects fall apart.
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How do you decide between prompting, retrieval and fine tuning?
What a strong answer shows: Judgement about cost and iteration speed, rather than reaching for the most sophisticated option available.
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Your retrieval returns plausible but irrelevant chunks. What do you do?
What a strong answer shows: Whether you have actually debugged a RAG system: chunking, embeddings, reranking and the query itself are all fair game.
Evaluation
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How do you know whether a change to a prompt made things better?
What a strong answer shows: This is the question that separates the field. Strong answers describe a fixed evaluation set built before the change, not vibes.
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How would you evaluate something with no single correct answer?
What a strong answer shows: Familiarity with rubric scoring, pairwise comparison and their biases, including the fact that a model judging its own output flatters it.
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What did you do about hallucination in something you shipped?
What a strong answer shows: Whether you treat it as a system problem, with grounding and citations and refusal paths, or as something to apologise for in the docs.
Cost, latency and operations
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Your feature works but costs too much per request. What are your options?
What a strong answer shows: Practical levers: smaller models for easy cases, caching, shorter context, batching, and knowing which of those hurts quality.
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How do you handle a provider outage or a rate limit in production?
What a strong answer shows: That you have thought past the happy path: fallbacks, queues, and what the user sees while it is happening.
Now practise it out loud
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Practise this interview freeQuestions people ask
What is the difference between an AI engineer and an ML engineer?
Broadly, AI engineers build products on top of existing models and are judged on the system around the model: retrieval, evaluation, latency and cost. ML engineers are more likely to train and serve models themselves. The titles overlap and the job description matters more than the label.
Do I need a machine learning degree for an AI engineer role?
Rarely. Most teams hiring for this are looking for strong software engineering plus evidence you have shipped something that used a model and can tell whether it works. Evaluation experience is scarcer than model knowledge.
What do AI engineer interviews test most often?
Evaluation. Almost anyone can wire up an API call, so interviews concentrate on how you measured quality, what you did when it regressed, and what the thing cost to run.
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