AI Engineer Interview Questions (2026)
The AI engineer role is newer and less standardized than software or data science, which means interviews vary widely — but a 2026 loop usually probes LLM fundamentals, retrieval-augmented generation (RAG), prompt and context engineering, evaluation, and the systems work of shipping AI features reliably. Many loops also keep a classic coding round, so don't neglect data structures.
Interviewers are looking for someone who understands both the models and the engineering around them: how transformers and attention work at a conceptual level, how to ground a model with retrieval, how to evaluate non-deterministic systems, and how to control latency and cost in production. Hand-waving about 'just calling the API' is a fast way to fail.
Because the field moves quickly, demonstrating that you reason from first principles — and that you ship and measure, not just prototype — matters more than reciting yesterday's benchmark numbers.
⚡ The Haggle Real-Time Advantage
Haggle is itself a local-first AI application — on-device LLMs, local RAG with sqlite-vec, and a bring-your-own-key architecture. In a live AI engineering interview it can transcribe the question and surface a precise definition or trade-off in real time, on your device.
Core Technical & Behavioral Questions
Explain how attention works in a transformer, at a high level.
Describe query/key/value, scaled dot-product attention, and why self-attention lets every token attend to every other — capturing long-range dependencies that RNNs struggle with. Mention multi-head attention's role and the quadratic cost.
How would you design a RAG system for a company knowledge base?
Walk the pipeline: chunking strategy, embedding model choice, a vector store, retrieval (top-k, hybrid with keyword), reranking, and prompt assembly. Discuss chunk overlap, stale-data refresh, and citation of sources.
How do you reduce hallucinations in an LLM application?
Ground with retrieval, constrain with structured outputs, lower temperature for factual tasks, add citation requirements, and verify with a second pass or rules. Stress evaluation: you can't reduce what you don't measure.
How would you evaluate a non-deterministic LLM feature?
Build a labeled eval set, use rubric-based or LLM-as-judge scoring, track regression across prompt/model changes, and combine offline evals with online metrics. Mention the cost and bias caveats of LLM-as-judge.
When would you fine-tune vs use RAG vs prompt engineering?
Prompt engineering first (cheapest, fastest). RAG for knowledge/freshness. Fine-tuning for behavior, format, or domain style that prompting can't reliably hit. Often a combination; justify by cost, latency, and maintainability.
How do you control latency and cost in an LLM product?
Model selection by task (small models for easy turns), prompt/context trimming, caching, streaming for perceived latency, batching, and routing. Quantify: smaller context and a cheaper model can cut cost an order of magnitude.
What are the trade-offs of running a model locally vs via a cloud API?
Local: privacy, no per-token cost, offline, but limited by hardware and model size. Cloud: top-tier models and scale, but cost, latency, and data leaving your device. Tie to the use case — exactly the trade-off Haggle is built around.
How would you choose an embedding model for retrieval?
Match the domain and language, check MTEB-style benchmarks, weigh dimension size vs storage/latency, and validate on YOUR data with a retrieval eval. Don't trust a leaderboard over a domain-specific test.
Implement a simple semantic search over a set of documents.
Embed documents and the query, store vectors, compute cosine similarity (or use a vector index like sqlite-vec/FAISS), return top-k. Discuss normalization and the speed/accuracy trade-off of approximate nearest neighbor.
Tell me about an AI feature you shipped and how you measured its success.
STAR. Emphasize the eval methodology and the production metric, not just the demo. AI engineers who ship and measure — rather than endlessly prototype — are what's being screened for.
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