Ai Engineering Interview

Generates high-signal AI Engineering / LLM Engineer interview questions by topic, level, and role. Covers LLM fundamentals, prompt engineering, RAG, vector DBs, agents, fine-tuning (LoRA/QLoRA), evals, observability, safety, and production systems. Trigger for requests like "give me interview questions on RAG", "quiz me on agents", "what are senior-level fine-tuning questions", or "interview questions for an AI engineer role".

Generate high-signal interview questions for AI Engineer / LLM Engineer roles.

Ask (or infer) the topic and level, then output exactly ONE complete question with a one-line note on what it's testing.

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Topics

LLM Fundamentals · Prompt Engineering · RAG Architecture · AI Agents · Fine-Tuning (LoRA/QLoRA) · Evaluation & Evals · LLM Observability · AI Safety & Guardrails · Production LLM Systems · LLM System Design · Multimodal AI · LLMOps · Edge AI · AI Governance · Embeddings · Real-Time AI

Levels

• Screening — Can they reason about LLMs as a component beyond "just call the API"?
• Mid — Full pipeline thinking: RAG, evals, agents, cost/latency trade-offs
• Senior — System design, failure modes, fine-tuning decisions, multi-agent, AI safety
• Staff — Platform thinking, LLM serving infra, eval-as-infrastructure, build-vs-buy

Output Format

For the question:

text

Q: [Question — scenario-based, trade-off, failure mode, or design. Never pure definition.]
Tests: [one line — what the interviewer is probing]

Prefer one question that can't be answered by Wikipedia + 5 minutes of reading. Do not add follow-up questions.

Scheduled Daily Variant

For the cron-triggered daily interview drill:

• Generate exactly ONE senior-level AI Engineer or LLM Engineer question.
• Rotate across the topic list in this skill instead of repeating the same cluster.
• No markdown tables.
• Use the default Q: and Tests: format.

Reference Files

• skills/ai-engineering-interview/references/question-bank.md — Curated questions by topic and level with expected answer shape, strong and weak signals, and possible follow-up prompts. Read this when the user wants a broader bank or asks for examples in a specific AI domain.
• skills/ai-engineering-interview/references/competencies.md — Interview rubric for scoring systems thinking, production judgment, safety awareness, and communication depth. Read this when calibrating difficulty or explaining what a strong answer looks like.