Data Engineer (AI) Interview Questions

Hallucination in RAG systems is the #1 production failure mode cited in AI engineering interviews. Your interviewer wants a systematic debugging framework — not a list of buzzwords. Learn how to measure faithfulness, fix retrieval precision, and layer mitigations the way senior engineers at Databricks and Meta actually ship RAG.

Enterprise RAG interviews test system design at scale: ACL-aware retrieval, audit logging, and ingestion pipelines for millions of documents. This is a staff-level question appearing at Microsoft, Salesforce, and Fortune 500 AI teams. Walk through a complete architecture with security boundaries and operational concerns.

**Usage** Separate small model for moderation latency.

**Hybrid** Traditional OCR for text layer + Gemini for semantic extraction.

**Ingestion** Transcode standards; chunk very long media if API limits apply.

**Ingestion** OCR for scans; preserve tables; metadata for citations.

**Session** Persistent realtime connection with session.update for instructions/tools; handle reconnect.

Hard AI Agents interview question on meta interview: content moderation agents — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on voice agents architecture — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on when rag fails: debugging playbook — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on multilingual rag challenges — architecture, trade-offs, eval, and production patterns.

Hard Google company question — document QA with OCR, layout, retrieval, multimodal models, and table/chart grounding.

Project question on synthetic data — distillation datasets, diversity, contamination, quality filters, and human review.

Hard scenario on PII in LLM systems — detection, redaction, DPA compliance, log minimization, and regional deployment.

Model question on RLHF — SFT, reward model, PPO alignment, and how human preferences change helpfulness vs hallucination trade-offs.

Project question on JSON schema mode, tool/function calling, validation loops, and agent reliability patterns.

Hard project question on LLM-as-judge — rubric design, position bias, human calibration, and production eval pipelines.