2036careers / AI engineering
Machine Learning Engineer
Own models in production: data pipelines, training, deployment and the MLOps that keeps predictions accurate, fast and cheap once real traffic hits them.
What you will do.
Real production work on live systems, not research.
- 01
Design, train and tune models for prediction, ranking, recommendation and computer vision problems in client products
- 02
Build and maintain the data pipelines that feed them: cleaning, feature engineering and versioned datasets
- 03
Deploy models behind APIs with proper MLOps: experiment tracking, reproducible builds, canary rollouts and rollback
- 04
Monitor drift, accuracy and cost in production and retrain before users notice, not after
- 05
Choose the boring, right-sized approach when a regression beats a neural net, and defend that choice
What you will bring.
If you have most of this and can show it, apply.
- Solid Python and the standard ML stack: scikit-learn plus a deep learning framework such as PyTorch or TensorFlow
- Models you have personally taken to production on a major cloud platform, and the scars to show for it
- Comfort with containers, CI/CD and infrastructure as code as part of everyday work
- Strong SQL and data modelling: most of this job is data before it is models
- The ability to explain a model decision to a business owner without jargon
Nice to have
- MLOps tooling such as MLflow or equivalent experiment tracking in anger
- Streaming or near-real-time inference experience
- Published work, open source or a blog that shows how you think
Apply.
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Also open: AI Engineer, AI Automation Engineer and Full-Stack Engineer, AI Products.