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AI Engineer interview questions & mock practice

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What interview questions are asked for an AI Engineer?

An AI Engineer interview in 2026 runs across 4 rounds — llm & genai fundamentals, rag & application design, coding round, system design & evaluation. Below are the most-asked AI Engineer interview questions and a focused prep plan. Rehearse every answer out loud with the free timed practice below before the real one.

  • LLMs
  • Prompt Engineering
  • RAG
  • Python
  • Vector Databases
  • LangChain
  • Embeddings
  • API Design

Free interview practice · AI Engineer

Practise your AI Engineer interview now — free

Step through the 13 most-asked AI Engineer questions one at a time, under a timer, just like the real thing. Jot your answer, then reveal what a strong answer covers. No signup needed to practise.

Interview rounds

The AI Engineer interview process

LLMs, prompt engineering, RAG and AI application development — the fast-growing generative-AI engineering interview at Indian product startups, GCCs and AI-first companies. Distinct from a classic ML engineer: the focus is building on top of foundation models, not training them.

1. LLM & GenAI fundamentals

How transformers and LLMs work, tokens, embeddings, context windows and prompting.

2. RAG & application design

Retrieval-augmented generation, vector databases, chunking and grounding.

3. Coding round

Python coding plus building an API around an LLM with a framework like LangChain.

4. System design & evaluation

Designing a production AI feature, handling cost, latency, safety and evaluation.

Most-asked questions

Most-asked AI Engineer interview questions

13 of the questions AI Engineer candidates are asked most often in India. Practise answering each one out loud in the timed practice above.

  1. 1. What is the difference between fine-tuning, prompt engineering and retrieval-augmented generation?
  2. 2. Explain how RAG works end to end, from a user query to a grounded answer.
  3. 3. What are embeddings and how are they used in semantic search?
  4. 4. What is a vector database and how does similarity search work?
  5. 5. What is a context window, and how do you handle inputs that exceed it?
  6. 6. What causes hallucinations in LLMs and how do you reduce them?
  7. 7. What is the difference between temperature and top-p sampling?
  8. 8. How would you chunk documents for a RAG pipeline, and why does chunk size matter?
  9. 9. What is the difference between zero-shot, few-shot and chain-of-thought prompting?
  10. 10. How do you evaluate the quality of an LLM application in production?
  11. 11. What are tokens, and why does pricing and latency depend on them?
  12. 12. How would you design a customer-support chatbot grounded in a company knowledge base?
  13. 13. What are guardrails, and how do you prevent prompt injection?
How to prepare

How to prepare for your AI Engineer interview

Understand how LLMs work at a usable depth: tokens, embeddings, context windows, sampling parameters and why models hallucinate.

Be able to design and explain a RAG pipeline end to end: ingestion, chunking, embedding, vector search, reranking and grounding.

Be fluent in Python and one orchestration framework such as LangChain or LlamaIndex, plus calling model APIs and handling streaming.

Know production concerns interviewers probe: cost and latency trade-offs, caching, evaluation, guardrails and prompt-injection defence.

Build and ship one real GenAI project, such as a RAG chatbot or document Q and A, that you can demo and discuss in depth.

Practise other roles

Interview prep guides

AI Engineer interview — FAQs

What questions are asked in an AI Engineer interview?

Common AI Engineer interview questions include: What is the difference between fine-tuning, prompt engineering and retrieval-augmented generation? Explain how RAG works end to end, from a user query to a grounded answer. What are embeddings and how are they used in semantic search? What is a vector database and how does similarity search work? Interviews usually run across 4 rounds — LLM & GenAI fundamentals, RAG & application design, Coding round, System design & evaluation. Practise them with the free timed practice on this page.

How many rounds are in an AI Engineer interview?

A typical AI Engineer interview has 4 rounds: LLM & GenAI fundamentals (How transformers and LLMs work, tokens, embeddings, context windows and prompting.); RAG & application design (Retrieval-augmented generation, vector databases, chunking and grounding.); Coding round (Python coding plus building an API around an LLM with a framework like LangChain.); System design & evaluation (Designing a production AI feature, handling cost, latency, safety and evaluation.).

How do I prepare for an AI Engineer interview?

To prepare for an AI Engineer interview: Understand how LLMs work at a usable depth: tokens, embeddings, context windows, sampling parameters and why models hallucinate. Be able to design and explain a RAG pipeline end to end: ingestion, chunking, embedding, vector search, reranking and grounding. Be fluent in Python and one orchestration framework such as LangChain or LlamaIndex, plus calling model APIs and handling streaming. Then rehearse every question out loud with the timed practice on this page before the real thing.

What skills do I need for an AI Engineer role?

Core AI Engineer skills tested in interviews include LLMs, Prompt Engineering, RAG, Python, Vector Databases, LangChain, Embeddings, API Design. OnJob shows you exactly which of these skills stand between you and a 100% match on every live AI Engineer job.

Is the AI Engineer mock interview practice free?

Yes. The questions, the answer guidance and the timed practice on this page are free and need no signup. With a free OnJob account you can also take timed role tests and free certification tests. Personalised AI role and skill tests are part of the paid plans.

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