GenAI / LLM Application Engineer Interview Prep & Rubrics

The loop for building LLM features in a product: prompting and structured output, retrieval, agents and tool use, evaluation, and the cost and safety questions that follow.

The loop, in order

1. 1Generative AI & Large Language Models26 questions · 7 topicsPrompting and structured output, context and tokens, adaptation, evaluation, inference economics, injection defence, and running LLM features in production.
2. 2RAG, Retrieval & Agents24 questions · 7 topicsRetrieval-augmented generation end to end: ingestion, embeddings and vector indexes, hybrid search and reranking, retrieval evaluation, permissions, and agentic tool use.
3. 3Backend Engineering30 questions · 7 topicsLanguage-agnostic service engineering: the protocols, boundaries, and failure handling that sit between a coding round and a full system design round.
4. 4System Design70 questions · 12 topicsOpen-ended design rounds broken into the building blocks they draw on, plus full case studies worked end to end with explicit trade-off reasoning.
5. 5Python22 questions · 7 topicsPython for backend, automation, and data roles, including the runtime behaviour that surprises candidates who only know the syntax.
6. 6DevOps & Cloud37 questions · 8 topicsPlatform and infrastructure questions for DevOps, SRE, and any backend role expected to own its service in production.
7. 7Behavioural & Culture Fit25 questions · 8 topicsThe round candidates most often under-prepare, treated as a skill with structure rather than small talk, including the rubric interviewers score against.
8. 8HR, Screening & Offer19 questions · 6 topicsThe recruiter and HR rounds that gate the technical loop and decide your compensation, where most candidates improvise and lose money.