Machine Learning / Data Engineer

Job Description

About the Role
An opportunity is available for a Hybrid Machine Learning / Data Engineer to join a team developing data-driven and machine learning solutions.
Working across both Data Engineering and Machine Learning, you will help take raw data through the complete journey from ingestion and transformation to feature engineering, modelling and production use.
This is not a traditional Data Scientist role and is not purely Data Engineering. You should be comfortable contributing across both disciplines and working across the broader ML lifecycle.
Key Responsibilities
Data Engineering

• Build and maintain data ingestion and transformation pipelines.
• Work with structured and unstructured data sources.
• Prepare and transform raw data into usable datasets.
• Implement data quality and validation checks.
• Develop feature engineering pipelines for machine learning use cases.
• Support reliable and repeatable data processing workflows.
• Troubleshoot pipeline and data-quality issues.

Machine Learning

• Build and train machine learning models.
• Develop and test features.
• Evaluate different modelling approaches.
• Apply appropriate model validation and evaluation techniques.
• Support the deployment of ML models into production.
• Monitor model performance following deployment.
• Contribute to model retraining and ongoing improvement.
• Connect modelling outcomes back to the underlying business problem.

Production Engineering

• Contribute to CI/CD and production deployment processes.
• Support model versioning and experiment tracking.
• Assist with monitoring and observability across ML systems.
• Help diagnose production issues.
• Contribute to scalable and maintainable ML applications.
• Work collaboratively with Senior Engineers, MLOps, platform and other technical teams.

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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