What to Look for When Hiring AI Engineers in 2025

Everyone is an AI engineer now. Or so the CVs suggest.

The reality is that the supply of genuinely experienced AI engineers — people who have shipped AI features to production, debugged failing RAG pipelines at 2am, and made pragmatic decisions about when *not* to use AI — is far smaller than demand.

Here is how to tell them apart.

What Genuine AI Engineers Know

A strong AI engineer understands the full stack of an AI application — not just how to call an API. They can speak to: prompt engineering and its limits, embedding models and vector search, model evaluation and reliability testing, latency and cost trade-offs, and when a traditional ML approach outperforms an LLM.

Interview Questions That Separate the Real from the Hyped

1. "Tell me about a time an LLM failed in production. What happened and how did you fix it?" 2. "How would you decide between fine-tuning and RAG for a knowledge-intensive application?" 3. "How do you evaluate whether a RAG pipeline is actually retrieving the right documents?" 4. "What are the cost implications of your architecture at 10x the current load?" 5. "Walk me through how you would implement a multi-agent workflow with error recovery."

Red Flags - Only references ChatGPT or Copilot — no production AI systems - Cannot explain embeddings or why semantic search matters - Has no opinion on model evaluation or how to measure quality - Claims to "know all the latest AI tools" but cannot explain the trade-offs between them

The FindCoder Approach All of our engineers pass rigorous technical assessments that mirror real production problems. When you hire through FindCoder, you get engineers who have already been validated — saving you the time and risk of the hiring process.