Hiring AI Developers in 2025: What Founders Need to Know

A good AI dev knows how to code models. A great one understands product impact, system trade-offs, and when not to use AI. They think like builders, not just technicians.

Start lean. One full-stack generalist with hands-on experience can take you from zero to MVP. As you scale, layer in specialists like MLOps engineers, data engineers, or LLM experts depending on your product's direction.

Focus on: ML fundamentals Real LLM fine-tuning experience MLOps and deployment skills Data engineering know-how Prompt engineering + API integration Surface-level knowledge won’t take you far.

Skip the AI jargon quiz. Instead, ask how they solved real-world model failures, deployed to production, or handled bad data pipelines. Practical answers beat theory every time.

Yes—for rapid prototyping or short-term R&D. No—if AI is core to your product. Outsource experiments, not your IP. Bring strategic AI work in-house as early as possible.