
AI hiring looks simple from the outside. It isn’t.
Many candidates can train a model. Few can deploy one. Others know theory but freeze when data drifts, latency spikes, or regulators ask questions. Titles don’t help. “AI Engineer,” “ML Engineer,” “Data Scientist.” Often interchangeable. Rarely equivalent.
The result shows up fast:
You feel it. Velocity drops. Confidence follows.
We filter for what actually matters. At Prometeo Talent, we assess applied machine learning, not academic comfort. We look at end-to-end ownership: data ingestion, feature engineering, model selection, evaluation, deployment, monitoring, and retraining.
Real systems. Real constraints.

AI fails most often at the handoff. Research to engineering. Engineering to product. Product to users.
Our process closes that gap.
We start by mapping your AI maturity. Data quality. Infra. Team structure. Risk tolerance. Then we define the role precisely: ML Engineer, Applied AI Engineer, or Research-leaning specialist. No vague profiles.
Candidates are screened on:
This isn’t volume hiring.
It’s precision.
The payoff:
Your competitors are learning every day.Their models get better. So do their decisions.