A machine learning (ML) developer, often called an ML engineer, turns data into working models and puts them into production: preparing features, training and tuning models, building APIs to serve predictions, and setting up monitoring and retraining so models stay accurate.
Hire ML developers to engineer, train and deploy machine learning models at production quality. Our ML engineers bridge data science and software engineering, building pipelines, APIs and monitoring around your models.
Every one of our machine learning developers is technically vetted, communicates clearly in English and works with sufficient time-zone overlap for daily collaboration. Choose full-time, part-time or hourly engagement, start quickly and scale your team as your roadmap evolves.
Skills our machine learning developers bring to your team from day one.
Share your requirements, review hand-picked profiles, interview the candidates you like and onboard within days. Your developer works as part of your team, using your tools and processes, while we take care of HR, infrastructure, security and replacements.
Engagement models: Full-time dedicated (around 160 hours per month), part-time (around 80 hours per month) and hourly or project-based arrangements.
Our ML developers use Python with scikit-learn, XGBoost, LightGBM, PyTorch and TensorFlow; track experiments with MLflow; and deploy on Azure Machine Learning, AWS SageMaker, Databricks or Kubernetes. They containerise models with Docker, serve them with FastAPI or Triton, and use CI/CD pipelines to release models safely on Azure and AWS.
Billing is monthly, and you can add people or reduce hours with reasonable notice as your needs change. ML developers work well alongside data scientists and data engineers. For a delivered solution rather than staff, see AI and ML development services.
Ready to hire ML developers? Tell us about your models and platform, and we will share suitable ML developers. Share your requirements.
Common questions about hiring machine learning developers through Prominent AI Services LLP.
ML developers focus on engineering and deploying models reliably, while data scientists focus on analysis and modelling.
Yes. They work with Azure ML, SageMaker and Vertex AI as well as open-source tooling.
Yes. You can add resources or reduce hours with reasonable notice as your project needs change.
It depends on experience, specialisation and engagement model. We share monthly or hourly rates with each profile so you can compare candidates.
Yes. They review features, data quality, algorithms and serving set-up, then retrain, tune or simplify the model and measure the improvement on held-out data.
They need representative data to train and test models. Access can be limited, masked or provided inside your own environment to meet your security policies.
Yes. They can set up experiment tracking, model registry, automated pipelines and monitoring sized to your team and number of models.
