Hire Machine Learning Developers

A data scientist focuses on analysis, model selection, and experimentation - work that typically lives in notebooks and produces insights or model files. A machine learning developer ships models to production: serving infrastructure, latency-optimised inference endpoints, drift monitoring, automated retraining pipelines, and integration with the rest of your stack. Both roles are valuable; most ML initiatives require both. If your model exists in a notebook but has not reached users, you need an ML developer, not more data science.

Production stack includes scikit-learn, XGBoost, LightGBM, and CatBoost for tabular ML; PyTorch and TensorFlow for deep learning; Hugging Face Transformers for NLP and fine-tuning; FastAPI and BentoML for model serving; MLflow and DVC for experiment tracking and model versioning; Feast and Tecton for feature stores; Evidently AI and WhyLabs for drift monitoring; Apache Kafka for real-time feature pipelines; Airflow and Prefect for batch pipeline orchestration.

Yes. Engineers work in your cloud accounts and deploy to your existing infrastructure. We have production experience with AWS SageMaker, GCP Vertex AI, and Azure Machine Learning for model training and serving, as well as self-managed deployments on Kubernetes for teams that want full infrastructure control. All code and configuration is in your repository and under your accounts from day one.

A focused ML engagement - feasibility assessment, data audit, model development, and production deployment with monitoring - typically runs $30,000 to $100,000 depending on scope and data complexity. A full MLOps pipeline setup alongside model development runs $80,000 to $200,000. Dedicated ML developer embedding starts at $12,000 to $18,000 per month for a senior ML developer with part-time PM. We scope before pricing and deliver fixed-cost proposals, not hourly estimates.

For a scoped feasibility assessment, we can typically start within two weeks of a signed agreement. The feasibility assessment runs two to three weeks and covers data quality evaluation, approach selection, and a clear recommendation before committing to a full build. This is the right starting point for most engagements - it confirms the approach is sound before significant budget is committed.

Yes. RaftLabs has shipped ML systems in healthcare (remote patient monitoring, clinical documentation), financial services (fraud detection, credit risk scoring), and logistics (demand forecasting, route optimisation). Engineers on regulated-industry engagements understand HIPAA data handling requirements, model explainability obligations for financial models, and audit trail requirements. Regulated-industry experience is not a checkbox; it changes how models are designed, evaluated, and documented.

Production models degrade because the world changes. We deploy monitoring for two types of drift: data drift (input feature distributions shifting away from the training distribution, detected via statistical tests on held-out reference data) and model performance drift (prediction accuracy declining as ground truth labels accumulate). Monitoring runs via Evidently AI or Arize with defined alert thresholds. When drift crosses a threshold, we trigger a retraining pipeline - retraining on a schedule without evidence is wasteful, but waiting for visible accuracy loss is expensive.