AI mock interview

AI Engineer interview questions & mock practice

A 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 with OnJob's free AI mock interview and get instant, specific feedback before the real one.

LLMsPrompt EngineeringRAGPythonVector DatabasesLangChainEmbeddingsAPI Design
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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 your AI mock interview.

  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 a 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. Practice all of them with instant AI feedback using OnJob's free mock interview.

How many rounds are in a 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 a AI Engineer interview?

To prepare for a 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 run a full AI mock interview on OnJob to rehearse out loud and get instant, specific feedback before the real thing.

What skills do I need for a 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 OnJob's AI Engineer mock interview free?

Yes. OnJob's AI mock interview is free to start (₹0) and gives you instant feedback on your answers. Pro (₹99/month) adds unlimited interview-prep AI alongside recruiter tracking and unlimited applications.

Free AI mock interview

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Rehearse every AI Engineer question out loud with OnJob's AI mock interview and get instant, specific feedback. Then apply to AI-matched jobs in one click — free to start.

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