AI Engineer Interview Questions: 30 Questions to Evaluate Real AI Skills

Key Takeaways

• Evaluate six core skills: LLMs, RAG, AI testing, agents, AI-assisted coding, and security.

• Ask for real examples of technical decisions and problem-solving.

• Use short exercises to assess practical experience.

• Score candidates based on clear criteria and evidence.

What to Evaluate in an AI Engineer

An AI engineer builds and adds AI features to software products. The role requires programming skills, knowledge of AI models, and experience testing and improving applications.

Interviews should cover six areas: LLM fundamentals, RAG and retrieval, AI evaluation, agents, AI-assisted coding, and security.

The questions should match the position. Junior candidates can demonstrate their skills through smaller projects, while senior engineers should also explain technical decisions and production experience.

At The Flock, AI Verified adds evidence of how professionals work with AI: how they bring it into their workflow, the decisions they make, how critically they review its output, and when they choose not to use it.

LLM Fundamentals: 5 Interview Questions

Large language models (LLMs) are AI models trained on large amounts of data to understand and generate text by predicting sequences of tokens. Engineers should understand how these models work, their limitations, and how to use them effectively in software applications.

1. How does a large language model generate a response?

Strong answer: Explains how models process tokens and predict the next token using patterns learned during training.

Red flag: Describes an LLM as a database that simply looks up answers.

2. What are tokens and context windows?

Strong answer: Explains how text is divided into tokens and how context limits affect what a model can process.

Red flag: Assumes larger context windows always produce better answers.

3. How would you improve a prompt that gives inconsistent results?

Strong answer: Uses clear instructions, examples, and tests to improve results.

Red flag: Changes prompts without checking their performance.

4. When would you use fine-tuning?

Strong answer: Explains when additional model training is useful and considers simpler options first.

Red flag: Recommends fine-tuning for every problem.

5. How would you choose an LLM for a product?

Strong answer: Compares quality, speed, privacy, reliability, and product needs.

Red flag: Chooses a model based only on popularity.

RAG and Retrieval: 5 Interview Questions

Retrieval-augmented generation (RAG) is a technique that allows AI applications to retrieve relevant information from external sources and use it as context when generating responses. Amazon Web Services explains how retrieval adds relevant context to model responses.

6. How would you build a basic RAG system?

Strong answer: Explains document processing, storage, retrieval, and how information reaches the LLM.

Red flag: Focuses only on the language model.

7. What are embeddings?

Strong answer: Explains how numerical representations help find information with similar meaning.

Red flag: Assumes similar meaning guarantees a correct answer.

8. How would you choose a vector database?

Strong answer: Considers search speed, data volume, filtering, and integration.

Red flag: Picks a database without considering project needs.

9. Why does document chunking matter?

Strong answer: Explains how splitting documents affects retrieval and context.

Red flag: Uses one chunking approach without testing it.

10. How would you improve irrelevant RAG results?

Strong answer: Reviews retrieval quality, filters, and search methods.

Red flag: Changes the LLM without checking retrieval.

Evaluating and Testing AI Systems: 5 Interview Questions

Evaluating and testing AI systems means measuring how well an AI application performs against defined criteria and checking its behavior across different scenarios. OpenAI's evaluation guidance recommends testing AI applications with clear goals and realistic examples.

11. How would you measure the quality of an AI application?

Strong answer: Defines clear success criteria, relevant metrics, and realistic tests.

Red flag: Relies on a few successful examples.

12. How would you identify hallucinations?

Strong answer: Checks generated answers against trusted information.

Red flag: Assumes well-written answers are accurate.

13. What is the difference between testing and monitoring an AI system?

Strong answer: Explains that testing checks specific cases, while monitoring tracks real usage.

Red flag: Relies only on testing before launch.

14. How would you test an AI feature before launch?

Strong answer: Tests normal requests, unusual inputs, errors, and performance.

Red flag: Tests only expected scenarios.

15. What would you do if an AI system started performing worse?

Strong answer: Reviews changes, test results, data quality, and logs.

Red flag: Makes changes without finding the cause.

AI Agents and Tool Use: 5 Interview Questions

AI agents are AI systems that can plan and execute multi-step tasks, use external tools, and make decisions about which actions to take based on their goals. Anthropic explains how agents work and when simpler workflows may be useful.

16. What makes an AI agent different from a regular chatbot?

Strong answer: Explains how agents select actions, use tools, and complete tasks.

Red flag: Calls every chatbot an autonomous agent.

17. How would you connect an AI agent to external APIs?

Strong answer: Covers tool setup, permissions, input checks, and API responses.

Red flag: Fails to validate API inputs or handle errors in API responses.

18. How would you handle errors in an AI agent?

Strong answer: Uses timeouts, retries, and human review when needed.

Red flag: Assumes every action will work.

19. When would you use more than one AI agent?

Strong answer: Explains when dividing tasks between agents adds value.

Red flag: Uses multiple agents without a clear reason.

20. How would you test an AI agent before deploying it?

Strong answer: Checks task completion, tool use, and error handling.

Red flag: Evaluates only the final response.

AI-Assisted Coding: 5 Interview Questions

AI-assisted coding is the use of AI tools to support software development tasks such as writing, reviewing, debugging, and testing code. Tools such as GitHub Copilot, Claude Code, and Cursor support these workflows. GitHub recommends reviewing and testing AI-generated code.

21. How do you use AI coding tools in your daily work?

Strong answer: Shares real examples of coding, testing, or debugging with AI.

Red flag: Makes general claims without examples.

22. How do you check AI-generated code?

Strong answer: Reviews logic, runs tests, and checks security.

Red flag: Uses generated code without reviewing it.

23. What would you do if an AI tool suggested an inefficient solution?

Strong answer: Finds the issue, compares alternatives, and improves the code.

Red flag: Accepts the suggestion because it works.

24. How would you use AI to understand an unfamiliar codebase?

Strong answer: Uses AI to explore the project and checks explanations against the code.

Red flag: Trusts generated explanations without verifying them.

25. How do you decide which coding tasks to give to AI?

Strong answer: Considers complexity, risk, and review needs.

Red flag: Delegates critical changes without checking them.

Security, Privacy, and Governance: 5 Interview Questions

Security, privacy, and governance in AI systems involve protecting sensitive data, preventing unauthorized access, and establishing controls for the safe and responsible use of AI. The OWASP Top 10 for LLM Applications identifies common AI security risks, while NIST provides guidance on managing them.

26. What is prompt injection?

Strong answer: Explains how malicious instructions can affect AI systems and how security controls reduce risk.

Red flag: Assumes system prompts prevent every attack.

27. How would you protect sensitive data in an AI application?

Strong answer: Uses access controls, encryption, and clear data-handling rules.

Red flag: Shares confidential data without checking how it is handled.

28. What security risks should teams consider when using RAG?

Strong answer: Covers document permissions, private information, and unsafe retrieved content.

Red flag: Assumes everyone can access all documents.

29. How would you control what an AI agent can do?

Strong answer: Limits permissions and requires approval for sensitive actions.

Red flag: Gives agents unrestricted access.

30. How would you manage AI security after launch?

Strong answer: Uses monitoring, regular reviews, and incident response plans.

Red flag: Treats security as a one-time check.

Practical AI Engineer Interview Exercises

Short exercises help interviewers see how candidates apply their knowledge.

Exercise	What to evaluate
Build a simple LLM feature	API integration and error handling
Improve a RAG system	Search quality and problem-solving
Check AI-generated answers	Accuracy and testing
Review AI-generated code	Code quality and judgment
Design an AI agent	Tool integration and security

Exercises should reflect the role and be reasonable to complete. Ask candidates to explain their decisions and how they would test the results.

How to Score AI Engineer Candidates

A consistent scoring system helps hiring teams compare candidates.

Score	Criteria
1 – Limited	Has difficulty explaining the concept
2 – Basic	Understands the basics but needs guidance
3 – Good	Applies knowledge to common tasks
4 – Strong	Shows practical experience and sound decisions
5 – Advanced	Handles complex problems and explains different approaches

Interviewers can score each of the six competencies separately and record examples that support their assessment. The importance of each skill should reflect the role, with the same criteria applied to all candidates interviewing for that position.

Hire AI Engineers With The Flock

Evaluating AI engineers well takes the right questions, a practical exercise and a scoring system applied the same way to every candidate, and even then the hardest thing to see in an interview is judgment: how an engineer decides where AI improves the work and what they do when its output is wrong.

Through Talent Solutions, The Flock adds validated AI engineers to teams that keep leading the work. Professionals are validated on language, personality and technology against the real work before joining a team, and AI Verified adds evidence of how they work with AI, from the decisions they make to how they review what it produces.

Frequently Asked Questions

1. What skills should an AI engineer have?

AI engineers need programming skills, knowledge of AI models, and experience with testing and software development. Depending on the role, they may also need RAG, AI agents, cloud, and security skills.

2. What is the difference between an AI engineer and an ML engineer?

AI engineers often focus on adding AI features to software products. Machine learning engineers usually work more closely with training, deploying, and maintaining machine learning models. Some responsibilities overlap.

3. How long does it take to hire an AI engineer?

Hiring time depends on the skills required, the candidate's experience, and the selection process. Clear job requirements and structured interviews can help teams make decisions faster.

4. How do you evaluate AI-assisted coding skills in an interview?

Ask candidates to explain how they use AI coding tools in real projects. A practical exercise can help assess how they review generated code, identify errors, run tests, and consider security risks. Look for clear technical reasoning, examples of independent problem-solving, and the ability to recognize when AI suggestions need improvement.

5. What practical exercise should you give an AI engineer?

Choose a short exercise that reflects the role's responsibilities, such as improving a RAG system, reviewing AI-generated code, or designing a simple AI agent. Ask candidates to explain their technical decisions, how they would test the solution, and how they would handle errors. Evaluate the process as well as the final result.