Hybrid Machine Learning / Data Engineer

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.

What You'll Be Expected to Do You will take hands-on responsibility for clearly defined technical problems and independently deliver solutions within an agreed technical direction.

You will:

• Build data pipelines, features and ML solutions.
• Make day-to-day engineering and modelling decisions.
• Work with Senior Engineers on more ambiguous or complex design decisions.
• Contribute to technical discussions and challenge approaches where appropriate.
• Continue developing your production engineering and architecture capability.
• Support multiple ML initiatives where required.

About You You will bring:

• Commercial experience in Machine Learning, Data Engineering or a hybrid ML Engineering role.
• Strong Python and SQL skills.
• Experience building data pipelines.
• Experience with data transformation and feature engineering.
• Hands-on experience developing and evaluating machine learning models.
• Exposure to deploying or supporting ML models in production.
• An understanding of model monitoring and retraining.
• Good software engineering practices.
• Experience in cloud-based data or ML environments.
• Ability to work with structured and unstructured data.
• Strong communication and problem-solving skills.

Desirable Experience Exposure to any of the following would be beneficial:

• Spark or Databricks
• Airflow or other orchestration tooling
• MLflow or experiment tracking
• Docker
• CI/CD
• PyTorch or TensorFlow
• NLP or document processing
• LLM or Generative AI
• Insurance, claims or pricing environments
•