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Last year, I quit an AI startup where I helped build a multimodal RAG system and the agent layer of a production web app. Before leaving, I designed the interview process to hire my replacement — and the results were surprising.

In this video I break down what companies actually expect from an AI engineer, and why the role is usually less about training models and more about integrating LLMs into real products.

Most “AI engineer” jobs are really full-stack developers building AI-powered systems — agents, workflows, RAG pipelines, vector databases, evaluation frameworks, and observability.

I walk through what I expected candidates to know in the interview, including:

• Structured outputs

• Evaluating when AI systems are wrong (evals)

• Observability and tracing prompts/token costs

• RAG architecture and document chunking

• Metadata and data freshness

• Why re-ranking dramatically improves retrieval quality

I also share what happened when we caught candidates cheating during the interview — and the biggest gaps I saw between AI hype and real AI engineering.

This experience ultimately led me to build the Parsity AI Accelerator, focused on teaching developers how to build production AI systems rather than just prompting chatbots.

If you’re trying to become an AI engineer or preparing for AI interviews, this video will show you what actually matters.