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# Data Engineer

- **Company:** London +
- **Location:** Nottingham, UK (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Business Software, Cloud Engineering, Computer Programming, Information Engineering, Data Transformation, Python (Programming Language), Machine Learning, Azure Machine Learning, Feature Store, Retrieval-Augmented Generation, Generative AI, Microsoft Fabric, Machine Learning Operations, Azure AI, Data Pipelines
- **Published:** October 30, 2026
- **Apply:** https://www.totaljobs.com/job/data-engineer/london-country-mortgages-job108247842

## About the Role

* Strong experience developing and deploying machine learning solutions
 * Excellent knowledge of machine learning techniques and methodologies
 * Experience working with large datasets and data transformation processes
 * Strong programming skills, particularly in Python
 * Understanding of data engineering and MLOps practices
 * Experience with cloud-based AI platforms such as Azure AI, Azure Machine Learning or Microsoft Fabric
 * Strong analytical and problem-solving skills
 * Passion for innovation and emerging AI technologies

## Description

We're looking for a Data & AI Engineer to join our growing technology team and play a pivotal role in delivering innovative AI solutions that create real business impact.

This is an exciting opportunity for someone who enjoys combining machine learning, data engineering and modern AI technologies to solve complex challenges. From predictive models to Generative AI solutions, you'll help design and build intelligent capabilities that support colleagues, customers and business growth.

What you'll be doing

 * Designing, developing and deploying machine learning models and AI solutions
 * Building predictive, recommendation and optimisation capabilities
 * Developing Generative AI solutions, including RAG architectures and agent-based frameworks
 * Creating and maintaining data pipelines, feature stores and training datasets
 * Implementing MLOps practices and model lifecycle management
 * Monitoring model performance, drift and accuracy to drive continuous improvement
 * Collaborating with software and platform engineering teams to integrate AI into business applications
 * Promoting responsible, governed and ethical AI practices