ML Engineer The ML Engineer will bridge the gap between data scientists' MVPs and production-ready ML solutions, focussing on optimising and scaling models. | - Build and maintain robust data pipelines for feature extraction, transformation, and model training.
- Maintain and enhance the existing solutions from the PoC phase to be production ready.
- Ensure pipelines are resilient, scalable, and capable of handling real-time or batch data.
- Maintain clear documentation on model experiments, data versions, and key decisions.
- Collaborate with PoC team to iterate on experiments and refine models efficiently.
- Refactor and optimise MVP models for production deployment.
- Implement model retraining pipelines for large-scale data processing.
- Develop APIs for model serving and integration with other systems.
- Collaborate with data scientists to understand model requirements and limitations.
- Implement model versioning and experiment tracking systems.
- Optimise model performance for both cloud (AWS, Azure) and on-premises environments.
- Develop solutions for model explainability and fairness in production.
- Work on integrating LLMs into existing ML pipelines and applications.
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