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# ML Engineer

- **Company:** THE OPPORTUNITY HUB UK LTD
- **Location:** Sheffield, UK
- **Experience:** Expert
- **Salary:** £47,500.0 - £64,000.0
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Data Cleansing, Data Infrastructure, Machine Learning, Tensorflow, Pytorch, Delivery Pipeline, Scikit Learn, Machine Learning Operations, Software Version Control
- **Published:** October 24, 2026
- **Apply:** https://www.jobs.service.gov.uk/jobs/6adb0b44b535da8c5347acdf/apply

## About the Role

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

Strong Python skills and familiarity with common ML frameworks (PyTorch, TensorFlow, scikit-learn)

Working knowledge of MLOps practices, including model versioning, monitoring and CI/CD for ML systems

Comfortable with cloud platforms (AWS, Azure or GCP) and the data infrastructure that sits underneath ML work

Good communicator. You'll need to explain technical decisions to people who don't live in the codebase

A mid-level engineer who can work independently within an established structure, not someone who needs constant direction

## Description

Machine learning has moved well past the pilot-project stage at most large organisations, and this is one of the ones that's actually done the work to put models into production at scale. This enterprise technology business, 250+ employees strong and an established name in its sector, needs a Senior ML Engineer to keep building on infrastructure that already handles serious volume. It's a well-resourced team with proper frameworks in place, not a scrappy start-up figuring things out as it goes.

Here's what you'll be doing:

Design, build and deploy machine learning models into production systems, working across the full lifecycle from data preparation to monitoring

Optimise existing models for performance, accuracy and cost, and know when a model needs retiring rather than tweaking

Collaborate with data engineers and platform teams to build pipelines that can handle enterprise-scale data volumes

Write clean, tested, maintainable code and review the work of colleagues on the wider team

Translate business problems into ML approaches that stakeholders outside engineering can actually understand

Contribute to technical standards and best practices that other engineers in the AI & Technology function will follow