The signals that mattered when you hired ML engineers in 2022 barely predict who ships reliable AI systems in 2026. "Trained a model on Kaggle" and "knows PyTorch" tell you almost nothing about whether someone can put an agent in front of real users without lighting your token budget on fire.
After 200+ projects, here's the evaluation framework we actually use.
First, know which tier you're hiring
There are three distinct roles people lump together as "AI engineer," and mixing them up is the #1 hiring mistake:
ML/Research engineers train and fine-tune models. You need these only if the model is your product.
