> Markdown version of [/jobs/ext/4555311-ml-engineer](https://www.wearedevelopers.com/jobs/ext/4555311-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# ML Engineer

- **Company:** THE OPPORTUNITY HUB UK LTD
- **Location:** Leeds, United Kingdom
- **Experience:** Expert
- **Salary:** £49,500 - £65,500
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Python (Programming Language), Machine Learning, Jupyter Notebook, Machine Learning Operations
- **Published:** November 4, 2026
- **Apply:** https://www.jobs.service.gov.uk/jobs/6aeada5245d38df994acbff9/apply

## About the Role

Solid commercial experience building and shipping machine learning models in Python

Comfort with the full ML lifecycle: data prep, training, evaluation, deployment, monitoring

Working knowledge of cloud platforms (AWS, Azure or GCP) and MLOps practices

Strong grounding in statistics and the judgement to know when a simpler model beats a flashy one

Clear communicator. You'll need to explain model limitations to people who don't live in Jupyter notebooks

A collaborative streak. This isn't a lone-wolf role

## Description

Machine learning teams in Leeds are no longer side projects bolted onto a tech department. They're core to how established companies compete, and this business, a growing SME in the AI & Technology space, has built its model infrastructure around that reality. The team ships models that touch real products, not slide decks. This Senior ML Engineer role sits right in the middle of that work.

Here's what you'll be doing:

Design, train and deploy machine learning models that solve genuine production problems, not research exercises

Build and maintain data pipelines that feed models reliably, at scale, without silent failures

Work closely with product and engineering teams to translate business questions into ML approaches that actually hold up

Monitor live models for drift, decay and performance drops, then fix what breaks

Write clean, tested code that other engineers can pick up without a translation guide

Contribute to technical decisions on tooling, architecture and where the team's AI & Technology roadmap goes next