Senior AI Engineer Interview Questions 2024-2025 | Generated by AI
Here are the most common and typical interview questions you’ll encounter for a Senior AI/Agent/LLM Engineer role in 2024–2025, especially at top companies (FAANG-tier, AI labs like Anthropic/OpenAI/xAI, scale-ups like Adept, Imbue, etc.).
They’re grouped by category and difficulty (most are senior-level, expecting deep understanding + production experience).
System Design & Architecture
- Design a scalable LLM inference serving system that can handle 10k+ QPS with <200ms p99 latency.
- How would you design a real-time AI agent that can browse the web, use tools, and maintain long-term memory?
- Design a retrieval-augmented generation (RAG) pipeline from scratch (vector DB choice, chunking, reranking, hybrid search, evaluation).
- How do you reduce inference cost of a 70B model by 5–10x while keeping quality degradation <2%?
- Design an evaluation framework for open-ended agent tasks (e.g., web shopping, research).
- How would you build a multi-agent system where agents collaborate (debate, hierarchy, etc.)?
LLM Fundamentals & Advanced Usage
- Explain how attention works from scratch (including Rotary Positional Embeddings, Grouped-Query Attention, Sliding Window Attention).
- Why does Llama 3/4 use RoPE instead of ALiBi? Pros/cons.
- Derive the scaling laws (Kaplan, Hoffmann “Chinchilla”, DeepMind “Emergent Abilities”).
- What causes “lost in the middle” in long-context models? How do you fix it?
- Compare Mixture-of-Experts (MoE) architectures (Mixtral, DeepSeek, Grok-1, Qwen-2.5-MoE). Why is activation sparsity hard in practice?
- How does quantization (GPTQ, AWQ, SmoothQuant, bitsandbytes) actually work? Trade-offs between 4-bit, 3-bit, 2-bit.
- What’s the difference between RLHF, DPO, KTO, PPO, GRPO, and when would you use each?
Agents & Tool Use
- How do you implement reliable tool calling / function calling with JSON mode vs ReAct vs OpenAI tools?
- Explain ReAct, Reflexion, ReWOO, Toolformer, DEPS, Chain-of-Verification.
- How do you prevent infinite loops in agent execution?
- How do you evaluate agent performance on benchmarks like GAIA, WebArena, AgentBench?
- How would you add long-term memory to an agent (vector store vs key-value store vs episodic memory)?
Training, Fine-tuning & Alignment
- Walk through the full fine-tuning stack: LoRA, QLoRA, DoRA, LoftQ, LLaMA-Adapter, IA³.
- How does QLoRA work under the hood (NF4, double quantization, pagined optimizers)?
- You have 10k high-quality instruction examples and want to fine-tune a 70B model on 8×H100s. Give the exact recipe.
- Explain constitutional AI, RLAIF, self-critique, process vs outcome supervision.
- How do you detect and mitigate reward hacking in RLHF?
Coding & Implementation (Live coding or take-home)
- Implement a simple ReAct agent from scratch (Python).
- Implement efficient sliding-window attention with flash-attention style caching.
- Build a basic RAG system with LangChain / LlamaIndex (they’ll judge architecture).
- Optimize a transformer forward pass for 128k context (memory efficient).
- Write a custom PyTorch autograd function for a new quantization kernel.
ML Fundamentals (they still ask seniors)
- Why does AdamW work better than Adam? Derive the weight-decay formulation.
- Explain label smoothing, teacher forcing, sequence-level vs token-level training objectives.
- What’s the difference between BLEU, ROUGE, BERTScore, LLM-as-a-judge, G-Eval?
- Derive the transformer loss function and explain why we ignore padding tokens.
Production & MLOps
- How do you monitor LLM outputs in production (drift, toxicity, PII leakage, prompt injection)?
- You notice 5% of your users are jailbreaking the model. How do you detect and defend?
- Compare vLLM, TGI, TensorRT-LLM, lmdeploy, Outlines, Guidance for inference speed.
- How do you do continuous fine-tuning / online learning safely?
Behavioral / Experience
- Tell me about the hardest production issue you fixed with LLMs.
- How did you ship an agent product that real users loved?
- Describe a time you reduced inference cost by >50%.
- What’s the worst hallucination disaster you’ve seen and how did you fix it?
Current Hot Topics (2025)
- How do test-time scaling methods work (DeepSeek R1, o1-style reasoning, process reward models)?
- Compare chain-of-thought, tree-of-thought, graph-of-thought, process vs outcome reward.
- What are the current best open-source reasoning models (Llama-3.1-405B, Qwen-2.5-72B-Instruct, DeepSeek-V3, Gemma-2-27B)?
- Thoughts on synthetic data generation loops (Self-Refine, STaR, Quiet-STaR, MCTS-based data synth).
Companies that ask the hardest questions right now: OpenAI, Anthropic, xAI, Adept, Scale AI, Helion, Imbue, Character.AI, Cohere, Mistral.
Prepare to whiteboard system design and to code on a laptop (no internet). Most senior loops are 5–7 rounds with heavy system design and deep-dive coding.
Good luck!