Outstaff ML Engineer (Middle)

Operationalising machine learning - moving from Jupyter notebooks to production-grade models - is where most companies stumble. itwebs' ML engineer outstaffing service gives you a dedicated Middle ML engineer who bridges that gap. Our specialist preprocesses raw data, trains models using PyTorch, TensorFlow, or scikit-learn, and deploys them as scalable REST endpoints with logging and versioning. They set up MLOps pipelines with tools like MLflow or Kubeflow, enabling automatic retraining, evaluation, and deployment. Crucially, they implement drift detection and monitoring so your models remain accurate as real-world data evolves. We manage every HR aspect - salary, contracts, equipment - so you can focus entirely on the AI use cases that drive revenue. The engagement starts with a data maturity audit and a proof-of-value model delivered in 2-4 weeks, demonstrating tangible ROI early. You keep all trained models, code, and documentation. If your project scope changes, you can flex the engagement up or down with one-week notice. This service is perfect for startups adding recommendation or personalisation features, companies with dormant data assets, or data teams that need an engineer to productionise prototypes. With itwebs, you don't need to hire a full in-house ML team. You get a hands-on, managed specialist who turns your data into features that improve user engagement and increase revenue.