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AI, Data & Machine Learning
Machine Learning Engineering
Most ML work fails at the handover, not the modelling. A notebook that scores well offline is not a system: it has no feature contract, no serving path, no way to tell when the world moved underneath it. We build the part that makes a model an operational asset rather than a demo.
What the work involves
Training & feature pipelines
- Reproducible training pipelines with versioned data and parameters
- Feature engineering with a shared definition across training and serving
- Experiment tracking so a result can be re-derived months later
Serving & integration
- Batch scoring and real-time inference endpoints
- Latency, throughput, and cost budgets set before the model ships
- Graceful degradation paths for when inference is slow or unavailable
Monitoring & lifecycle
- Data and prediction drift detection with alerting
- Scheduled and triggered retraining with promotion gates
- Shadow deployment and champion/challenger evaluation in production
What you get
- Reproducible training pipeline under CI
- Serving endpoint with latency and cost budgets
- Drift monitoring and a documented retraining policy
Tools we reach for
- Python
- PyTorch
- scikit-learn
- MLflow
- Docker
- Kubernetes
- Azure ML
Engagement shape
Project-shaped, typically 8–16 weeks to a production-served model, or embedded alongside your data team.
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Need this on your team?
Tell us the problem and we’ll give you an honest read on whether this is the right discipline for it.