AI Expert Network matches businesses with vetted machine learning engineers. Our experts build predictive models, recommendation systems, computer vision pipelines, and ML infrastructure. Rates typically range from $80 to $250 per hour.
Connect with experienced machine learning engineers. Build predictive models, recommendation systems, and custom ML pipelines tailored to your data.
A machine learning engineer takes messy business data and turns it into a model that runs in production and holds up over time. That means feature pipelines, training, evaluation, and the monitoring that catches drift before it costs money.
The line that matters is production. Plenty of people can fit a model in a notebook. Far fewer can ship one that survives real traffic, retrains cleanly, and fails safely when the input looks nothing like the training set.
Ask for a model they put in front of real users, and how they measured whether it worked. Strong engineers talk in business terms, dollars saved or errors avoided, not just accuracy on a held out set.
Push on failure. A serious ML engineer can tell you how their model behaves on out of distribution input and what guardrails they added so a bad prediction never silently ships.
On AI Expert Network, machine learning engineers cluster between $80 and $250 per hour, with deep learning, MLOps, and computer vision specialists at the higher end. A scoped model, from data audit to deployed endpoint, is usually a few weeks of focused work rather than an open ended contract.
A machine learning engineer designs, trains, and deploys predictive models. They handle data pipelines, feature engineering, model evaluation, and production deployment across classification, regression, and deep learning tasks.
Machine learning engineers on AI Expert Network typically charge $80 to $250 per hour. Specialists in deep learning, MLOps, or computer vision are on the higher end.
Core tools include Python, PyTorch or TensorFlow, scikit-learn, pandas, SQL, and cloud ML platforms like SageMaker or Vertex AI. Experience with MLflow, Airflow, and Docker is common.