Hire machine learning engineers to build and run your models
A machine learning engineer trains, tunes, deploys and monitors models on a company's own data. They turn data and research into production systems that are accurate, fast and cost-controlled. Offshore Employee places dedicated ML and MLOps engineers who join your team and work in your stack.
Skills we vet for
- Python, PyTorch, scikit-learn, XGBoost and LightGBM
- Feature engineering and feature stores
- Experiment tracking (MLflow, Weights & Biases)
- Deployment and serving (Docker, Kubernetes, ONNX, vLLM)
- Monitoring and drift detection
- GPU and cloud cost management (AWS, Azure, GCP)
What ML engineers build
- Recommendation and ranking systems
- Forecasting and pricing models
- Computer vision and NLP models
- Fine-tuned language models
- ML platforms and MLOps pipelines
How we vet every machine learning engineer
Technical ability first, CV second. You only meet people who have already shown they can do the work.
Our vetting process- Initial screening of English, communication and remote-work readiness
- Practical technical assessment, reviewed by a panel of senior engineers
- Technical interview with a lead architect or lead engineer on system design and past production work
- Reference checks with past managers and senior colleagues
Hiring a machine learning engineer: questions, answered.
What is the difference between an ML engineer and an MLOps engineer?
ML engineers build and improve models; MLOps engineers build the platform that deploys, monitors and retrains them. Many senior ML engineers do both.
Do I need an ML engineer if I use ChatGPT or Claude APIs?
Usually not at first. An AI engineer is the better first hire. Add an ML engineer when you need custom models or fine-tuning on your own data.
Other AI talent we place
Ready to hire your machine learning engineer?
Tell us what you're building. We'll help you find the people to move it forward.