Preparing for AI engineer interviews across ten topics — ML fundamentals, system design, LLM application architecture, coding, paper reading, and behavioral. Each post focuses on one interview dimension with core concepts, common question patterns, and practical strategies.
AI Engineer interviews go beyond ML — big tech emphasizes system design and coding, startups look for end-to-end delivery, and AI-native companies test LLM engineering depth. Strategy: identify your target company types first, then allocate prep time across six dimensions (ML fundamentals, system design, LLM applications, coding, paper reading, and behavioral).
ML fundamentals interviews don't test formula memorization — they test whether you can explain concepts intuitively and hold up under follow-up questions. High-frequency topics: the practical meaning of bias-variance tradeoff, the selection logic for L1/L2 regularization, why cross-entropy beats MSE for classification, SGD vs. Adam tradeoffs, and how precision/recall priorities differ by scenario.
Deep learning interviews don't ask you to derive backpropagation — they test whether you can explain the design intuition behind architectures. High-frequency topics: CNN's locality and translation invariance, why the evolution from RNN to Transformer was necessary, self-attention computation and complexity, BatchNorm vs LayerNorm use cases, and common training tricks (learning rate scheduling, gradient clipping, mixed precision).
The dividing line in LLM interviews is whether you've actually used these things. High-frequency topics: BPE tokenization logic and multilingual challenges, pretraining objectives (CLM vs MLM), three levels of fine-tuning (full/LoRA/prompt tuning), RLHF workflow and failure modes, prompting as engineering practice, and the difficulty of LLM evaluation with current methods.
The core of ML System Design interviews isn't choosing the model — it's how to turn a business objective into a system that's deployable, monitorable, and iterable. Interviewers want to see if you can: translate business goals into ML objectives, design data pipelines and feature stores, choose reasonable serving strategies, and plan monitoring and A/B testing.
LLM Application Design is the hottest new interview topic in 2025-2026. Key focus areas: RAG pipeline chunking/retrieval/reranking design, agent tool-use and planning loops, context window management strategies, guardrails and safety design, and LLM application evaluation methods. Interviewers especially value whether you've hit real-world pitfalls.
AI Engineer coding interviews aren't identical to SWE — beyond LeetCode medium, you'll face ML-flavored problems (implementing a tokenizer, writing a batch inference pipeline, handling sparse matrices). Strategy: practice LeetCode medium to 70% pass rate, then spend remaining time on numpy/pandas operations, data processing pipelines, and ML-related programming problems.
Paper reading interviews don't test whether you've read that specific paper — they test whether you can quickly understand a new method and identify its limitations. AI-native companies (Anthropic, OpenAI) particularly favor this format. Strategy: practice reading a paper in 30 minutes and verbally stating contribution + limitation, build your own must-read list, and practice summarizing each paper in three sentences.
MLOps interviews test whether you have experience pushing models to production. Key topics: ML pipeline CI/CD (how it differs from software CI/CD), model registry and version management, A/B testing design and pitfalls, inference scaling strategies (horizontal scaling, model compression, caching), and production monitoring and alerting design.
Behavioral interviews aren't about improvisation — they're about a pre-prepared story library. AI Engineer behavioral interviews have unique focus areas: AI ethics (bias, fairness, privacy), technical decision impact narratives (why you chose this model/architecture), and experience driving ML projects across teams. Strategy: build 8-10 STAR stories, practice each until you can deliver it in under 2 minutes.
Read each company's own interview guidance first: Meta now expects candidates to use a built-in AI assistant, while Anthropic defaults to no AI. For question banks, pick ones that cite sources and are still maintained this year, such as the alexeygrigorev field guide, ombharatiya, and alirezadir. Only 2 of the 12 popular repos link sources for their questions, and some keep answers behind a paywall.
Answer RAG interview questions in four steps: the three-stage pipeline, where each stage fails, which of nine variants patches which hole, and the 6 kinds of problems RAG cannot solve. Self-RAG, CRAG and Adaptive-RAG add judgment; Graph RAG and KG-RAG change the index; CAG skips retrieval; Agentic RAG and Reasoning RAG hand decisions to the model.
Agent interviews keep returning to eight topics: the agent loop, structured output, function calling, large tool sets, memory, design trade-offs, MCP versus A2A, and prompt caching. This post strings them into one line (concept, mechanism, how to answer) and corrects when the 2026-07 MCP spec changes actually landed.
Prompt engineering shapes a single instruction, context engineering decides what the model sees right now, and harness engineering wraps the whole non-deterministic system. Bsharat's 26 principles, Breunig's four context failures, and OpenAI's roughly one-million-line, zero-handwritten-code harness experiment each map to one layer. Evaluation is the piece teams skip most, and it belongs in a CI gate that blocks silent regressions.
Seven LLM engineering topics that come up often in interviews: LoRA/QLoRA and fine-tuning vs RAG, RLHF/DPO/GRPO alignment, inference optimization (PagedAttention gives vLLM 2-4x throughput), sampling and decoding, LLM evaluation, the OWASP LLM Top 10 (2026 edition), and hallucination. Each section covers the concept, the mechanism, and how to answer.
Interview follow-ups usually land on mechanisms: why divide by √d_k, how AdamW differs from Adam plus L2, why a high AUC still isn't shippable. This post walks through concept, mechanism and how to answer, then ends with 3 runnable numpy exercises.
Four system design questions that come up often (enterprise RAG, an LLM serving stack, a customer-support agent, online A/B testing with a data flywheel), three Python problems (dynamic batching, sampling, backoff retries plus a token bucket), and a STAR skeleton for behavioral rounds. Each design question has a speakable answer and a self-check list, and all code was actually run.