ML Engineer

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.