AI Engineer Interview Guide for Java Developers with Vighneshwar Bhat

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AI Engineer Interview Guide for Java Developers (2026)

Spring AI · RAG · Agents · MCP · System Design

You already know how to build backend systems that are reliable, secure and scalable. AI engineer interviews now test whether you can do the same with LLMs: answers grounded in your data, agents that don't go rogue, costs that don't explode, and a way to prove quality. This guide prepares you for exactly that, from a Java developer's point of view.

Who it's for

Java and Spring developers with 3–10 years of experience who are moving into AI engineering roles, or who already build features on top of LLM APIs. You don't need to train models or know the maths behind them.

What's inside (124 pages)

• 98 core interview questions across 7 chapters, each written the way a senior engineer would answer out loud. Every answer leads with the point, works through a concrete example, and ends with the follow-up interviewers usually ask.
• LLM fundamentals: tokens, context windows, embeddings, hallucination, and when to use prompting, RAG or fine-tuning.
• Frameworks and prompting: Spring AI 2.0 and LangChain4j, structured output, tool calling, chat memory, reasoning models and context engineering.
• RAG in depth: chunking, hybrid search, reranking, permissions, keeping indexes fresh, tables in PDFs, embedding migrations, and indexing Slack.
• Agents and MCP: agent loops with safety limits, the 2026-07-28 MCP specification, building an MCP server in Spring, and tool-poisoning risks.
• Testing and evaluation: a Java test pyramid, measuring groundedness, LLM-as-judge, CI/CD for LLM apps, monitoring, and RAGAS from Java.
• Security and governance: the 2026 OWASP Top 10 for LLM Applications, prompt injection vs jailbreaks, the "lethal trifecta", and an engineer's view of the EU AI Act.
  • Java and Spring in production:Spring Security and passing the user's identity to tools
  • async AI jobs with Kafka and the outbox pattern
  • deployment
  • virtual threads with deadlines
  • transactions
  • caching and rate limits
  • cost estimation
• 26 incident scenarios, such as "the bot invents a refund policy", "the cloud bill doubled" and "an agent cancelled the wrong order". Each one walks through clarify, contain the damage, diagnose, fix and prevent, with the signals you'd see in production. Seven include a model spoken answer.
• 9 system designs, including an enterprise RAG assistant, an LLM gateway, an insurance-claims agent, text-to-SQL, a multi-tenant AI platform and a document ingestion pipeline. Each starts with the interview question as it would be asked. Deep dives cover numbers, trade-offs, failure modes, evaluation, cost and scaling, plus a "first 60 seconds" opener.
• Senior-level depth: 37 answers include a "Going further" section on rollout, failure cases and trade-offs.
  • Interview day prep:Java coding tasks
  • two timed mock interview scripts
  • behavioural story templates
  • a 7-day plan
  • a one-page cheat sheet
  • a rapid-revision list of answers
  • a glossary
• Bonus: an index of 37 common Spring AI questions, and an optional appendix on self-hosted inference for ML-leaning roles.

What makes it different

• Written for Java developers. It's written for Java developers throughout, not a Python guide with Java added on.
• Up to date for October 2026: Spring AI 2.0, Java 25, the latest MCP specification and the 2026 OWASP list.
• Easy to navigate. Every cross-reference in the PDF is clickable, and the bookmarks list every question.

What you get

• The PDF (124 pages)

FAQ

• Do I need ML or Python experience? No. The guide is written for Java backend engineers who use hosted models.
• Are these questions are useful for interviews? These questions are designed based current market expectations.
• Is this a complete Java or Spring interview guide? No. It covers the AI layer and the Java and Spring parts that connect to it, so pair it with your usual backend preparation. For complete Java backend interview you can refer here
• Will it go out of date? AI frameworks change fast. Versions are dated in the guide, and each one points you to the official source to recheck.