#llm #ml #systemdesign #interview

Chatted with AI tech leads hiring AI engineers.

Here's the stack they look for in interviews ↓

① 𝗦𝗪𝗘 + 𝗠𝗟 𝗕𝗮𝘀𝗶𝗰𝘀
SWE → Python, Docker, Version Control, APIs
ML → Data prep, feature eng, ML algos/evals

② 𝗟𝗟𝗠 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀
• DPO
• RLHF
• Quantization
• Transformers
• LoRA, QLoRa
• Flash Attention
• Diffusion Model
• RAG vs Fine-Tune
• Mixture of Experts
• DeepSeek Architecture

*No need experience in training these from scratch. Just need conceptual understanding.

③ 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻
• RAG
• MCP
• DSPy
• CoT + ReAct
• Context Engineering
• Framework → LangGraph, PydanticAI

④ 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻
Problem → Scope → Design → Optimize (Scale, Cost, Availability)

• Design ChatGPT clone
• Design Browser agent
• Design SQL agent

*Knowing how to optimize for scale (10K vs 10M users, costs, 99% availability, reduce latency from 10 to 3 seconds).

⑤ 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲
Not optional. They aren't going to hire someone who's built an agent that works locally.

Knowing how to build and deploy agents that work on cloud services matter. AWS, GCP, Azure and etc, just pick a platform, and deploy it.