For companies
Builds agentic systems and tool-use that hold up in production. We find the ones who can actually do it, and we figure out the right way to bring them to your problem.
An applied AI engineer builds the systems around a model: agents, tool-use, retrieval, and orchestration. The model is one part. The job is making the whole thing reliable enough to put in front of real users.
The demo of an agent is easy. The version you can trust with a customer’s money is not. Applied AI engineers spend their time on the second one, which is mostly the unglamorous work of failure handling and measurement.
Most hiring filters on credentials and years. The thing that makes a applied ai engineer good does not show up there. It shows up in how they work, which means you have to watch them work to see it.
That is what we do. We watch people work instead of reading resumes, so the person we send you is calibrated on the actual job, not the interview. Sometimes that is a hire. Sometimes it is a project or a person embedded for a while. We work out the shape with you.
An engineer who builds production systems on top of language models: agents, retrieval, tool-use, and the orchestration that makes them reliable.
No. ML engineers often train and serve models. Applied AI engineers build the product and systems around models that already exist.
In the US, total compensation typically runs $180k to $260k for engineers who have shipped reliable agentic systems.