Meta AI MLOps Engineer Interview Questions

Self-hosting Llama 3 is a infrastructure-heavy question for ML platform and AI engineer roles at Meta-adjacent companies. Expect deep dives on quantization, vLLM, GPU sizing, and the TCO math that determines build vs buy decisions.

Company/scenario question on open vs closed LLMs — self-hosting economics, compliance, upgrade velocity, and hybrid strategies.

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.

Advanced scenario question on why LLMs hallucinate — next-token prediction vs fact verification, root causes, and production defenses asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

Part 2 of the LLM hallucination handbook — factual, citation, reasoning, math, code, temporal, context, and tool-use hallucinations; decoding risks; RAG failure modes; production case study.

Part 3 of the hallucination handbook — production defense layers from retrieval and prompting through guardrails, citations, confidence scoring, eval frameworks, and enterprise architecture. Asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

LLM interview topic on distillation — teacher/student training, latency/cost wins, and quality regression risks.

**Constraints** No phone-home telemetry; offline license compliance documentation.

**Training** Rank selection, learning rate, epochs; validation loss + task evals.

**GPTQ/AWQ** GPU inference friendly; integrate with vLLM/TGI depending on support.

**vLLM** High throughput continuous batching; popular for multi-tenant APIs.

**3.1** 128k context, stronger instruction following — update inference stack for long context KV cache.

Cross-language prompt design covering translation drift, code-switching, locale formatting, and eval gaps in multilingual products.

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

Hard scenario on jailbreaks — DAN, prompt injection, alignment limits, red-teaming, and layered defenses.

Hard project question on INT8/INT4 quantization, knowledge distillation, and serving smaller models for cost and latency.

Model question on multimodal LLMs — image/audio tokenization, document QA, OCR pipelines, and latency/cost trade-offs.

Decoding parameters interview question — how sampling controls randomness, creativity, and hallucination risk in production.

Foundational model question on BPE/tokenizers — why token counts differ from words, and production impact on cost, limits, and RAG chunking.

Hard Llama/inference question on continuous batching and GPU efficiency.

Medium RAG interview on hard negatives for embedding/reranker quality.

Model-based interview on DPO vs RLHF — preference pairs, training simplicity, and production trade-offs.

**Dimensions** Quality, cost curve, latency, compliance, ops burden, roadmap velocity.

**Analysis** Rank features by volume, quality bar, data sensitivity benefiting on-prem.