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# Machine Learning Engineer

- **Company:** THE OPPORTUNITY HUB UK LTD
- **Location:** Newcastle upon Tyne, UK
- **Salary:** £47,000.0 - £66,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Software Debugging, Fraud Prevention and Detection, Machine Learning, Recommender Systems, Tensorflow, Management of Software Versions, Pytorch, Machine Learning Operations
- **Published:** October 23, 2026
- **Apply:** https://www.jobs.service.gov.uk/jobs/6adb5fb7b535da8c534a1608/apply

## About the Role

Solid experience building and deploying machine learning models in a production environment

Strong Python skills, plus familiarity with frameworks such as PyTorch, TensorFlow or scikit-learn

Understanding of MLOps practices: versioning, CI/CD for models, monitoring

Comfortable working with cloud platforms (AWS, Azure or GCP) and containerised deployments

Good communication skills. You'll need to explain model decisions to people who aren't engineers

A methodical approach to debugging and a genuine curiosity about why a model is behaving the way it is

## Description

Join a enterprise AI & Technology company as a Machine Learning Engineer in Newcastle.

Machine learning has moved well past the pilot-project stage at most large organisations. It now sits inside the core systems that run the business, from fraud detection to forecasting to customer-facing recommendation engines. This employer is a long-established name in AI & Technology, with the scale and infrastructure to put models into production rather than leaving them in a notebook. That's the gap a lot of mid-sized firms can't close. This one already has.

Here's what you'll be doing:

Design, train and validate machine learning models for production use cases across the business

Build and maintain data pipelines that feed model training and inference at scale

Work with data scientists and software engineers to move models from prototype to live deployment

Monitor model performance in production and retrain or adjust when drift creeps in

Write clean, tested, documented code that other engineers can actually pick up and extend

Contribute to technical decisions on tooling, architecture and best practice as the team grows