The Applied-AI FDE Interview: RAG, Agents & Model Serving
At AI-inference and AI-infrastructure companies, a Forward Deployed Engineer spends much of the job wiring LLMs into customer data and getting them to run fast, cheap, and reliably. The interview reflects that: expect questions on retrieval-augmented generation (RAG), agents, evaluation, and inference performance. You do not need to be a researcher, but you must speak these trade-offs fluently.
RAG and evaluation
The most important instinct to demonstrate: when a RAG assistant answers wrongly but confidently, it is usually a retrieval failure, not a model failure — the right context never reached the model. Strong candidates say "I would check what was retrieved before blaming the model," then talk about chunking, hybrid (vector + keyword) search, reranking, and keeping the index fresh.
Evaluation matters too: use a fixed set of question-and-expected-answer pairs to measure changes, and separate retrieval quality from answer quality so you know which half broke.
Agents and inference performance
For agents, know the guardrails: a max-step budget, explicit termination conditions, and idempotent tools so a retried action is safe. For serving, speak the numbers: latency vs. throughput, time-to-first-token, p99 tail latency, batching and continuous batching, quantization (FP16 to INT8 to INT4), and cold starts. When asked to optimize, always ask which metric hurts — latency, throughput, or cost — before naming a lever.
Practice applied-AI scenario drills free
Practice applied-AI scenario drills free →Frequently asked questions
What AI topics are tested in an FDE interview?
At AI-infrastructure companies, expect retrieval-augmented generation (RAG), agents and tool use, evaluation, and inference performance (latency, throughput, batching, quantization, cold starts). The focus is applied judgment and trade-offs, not research-level depth.
How do I answer a RAG debugging question?
Start by checking retrieval — a confident wrong answer usually means the right context was never retrieved. Discuss chunking, hybrid vector-plus-keyword search, reranking, and index freshness, and mention evaluating with a fixed question-to-expected set.
What inference metrics should I know for an FDE interview?
Latency vs. throughput, time-to-first-token, p50 vs. p99 latency, batching and continuous batching, quantization levels (FP16/INT8/INT4), and cold starts. Always tie an optimization to the specific metric that hurts.