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Rahul Agarwal
Founder | Agentic AI...ย โขย 5h
Hot take for AI hiring. Iโm not impressed by people showcasing dozens of RAG pipelines and agent demos. Flashy projects are easy. Production thinking isnโt. When evaluating an AI engineer, the conversation goes somewhere else entirely. Can you design the complete architecture, from raw data ingestion pipelines to reliable model serving layers? Where will the system slow down? Where does it break first? Explain how you would estimate infrastructure costs and systematically bring those numbers down. Latency matters too. What tradeoffs would you accept between faster responses and slightly lower output quality? Do we even need self-hosted language models here, or are managed APIs the smarter choice? Tell me your reasoning. How would you fine-tune models using real user behavior while keeping deployment stable and scalable? Letโs talk data. How would you build the dataset, select loss functions, and maintain solid MLOps practices? Which storage layer makes sense here - vector database, relational database, or NoSQL system? And how do you measure success? What metrics matter? How do you monitor them? How would you investigate failures and debug strange outputs in production environments? Now the fun part. Could you solve this problem without LLMs or vector databases at all? Maybe classical information retrieval works. Maybe simple heuristics win. Walk through tokenization and embeddings from first principles. Because hereโs the truth. Iโve seen teams spend months building complicated AI systems that never reach production. Hype creates demos. Fundamentals build resilient systems. And thatโs what great engineers focus on.
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