AI Engineer Interview Questions UK: 8 Real Questions and What Interviewers Want

Applying for a AI Engineer job in the UK? Here is what you are likely to be asked. These questions come from our AI Engineer question bank of 25 questions, written for UK employers at mid-level level.

Eight AI Engineer interview questions, and what the interviewer is thinking

1. What does an AI engineer do day to day, and how does it differ from a data scientist?

Why they ask: Checks role understanding.

2. What is retrieval-augmented generation, and when would you use it instead of fine-tuning?

Why they ask: The most common LLM design question.

3. How would you evaluate an LLM application before putting it in front of users?

Why they ask: Evaluation is the hardest part of LLM engineering.

4. How do you reduce hallucinations in an LLM application?

Why they ask: Core practical concern.

5. What is prompt injection, and how would you defend against it?

Why they ask: Security of LLM systems.

6. How do embeddings and vector databases work, and how would you choose a chunking strategy?

Why they ask: Core RAG implementation detail.

7. Your customer-facing chatbot is giving confident but incorrect answers about company policy. What do you do?

Why they ask: Real incident.

8. Tell me about a time you took an ML or LLM feature from prototype to production.

Why they ask: Delivery evidence.

A sample answer to question 1

What does an AI engineer do day to day, and how does it differ from a data scientist?

An AI engineer takes models, increasingly foundation models and LLMs, and turns them into reliable products. My day might involve building a retrieval-augmented generation service, writing evaluation suites, integrating APIs from providers like Azure OpenAI, deploying with containers and CI/CD, monitoring latency, cost and quality, and fixing issues like hallucinations or prompt injection. I work closely with product managers, data engineers and security. A data scientist focuses more on exploring data, statistical modelling and experimentation to generate insight or prototypes, while an AI engineer focuses on software engineering, productionisation and operating AI systems at scale. There is overlap, and I value statistical thinking in my own work, but my emphasis is on building robust, safe and maintainable systems that users can depend on.

Common mistake to avoid: Describing only prompting; saying the roles are identical

Want the rest?

Full model answers, key points and common mistakes for all 25 questions are on the AI Engineer interview questions page. The first five are free, no sign-up needed.