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# Machine Learning Engineer

- **Company:** Cheltenham
- **Location:** Cheltenham, UK
- **Salary:** £200,200.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Cloud Database, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Azure Machine Learning, Unstructured Data, Feature Engineering, Pytorch, Deep Learning, Performance Monitor, Machine Learning Operations, Data Pipelines
- **Published:** September 24, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/machine-learning-engineer/48041099

## About the Role

improve existing solutions.Essential Skills & ExperienceStrong commercial experience as a Machine Learning Engineer. Experience designing and deploying machine learning models in production environments. Proven expertise in: Recommendation Systems Personalisation Models Ranking Algorithms User Behaviour Analysis Strong Python development skills and experience with machine learning frameworks. Experience building scalable data pipelines and feature engineering processes. Knowledge of experimentation methodologies, including A/B testing. Experience handling large-scale structured and unstructured datasets. Strong understanding of MLOps, model deployment, monitoring, and lifecycle management. Excellent communication and stakeholder engagement skills.Desirable SkillsExperience with deep learning frameworks such as TensorFlow or PyTorch. Experience working with cloud-based data and ML platforms. Exposure to real-time recommendation systems and large-scale personalisation products. Experience
within customer-facing digital or media environments. If you receive suspicious outreach claiming to be from us, please contact us via the ManpowerGroup website.JBRP1_UKTJ

## Description

and maintain scalable data pipelines to support model training and feature engineering. Work with structured and unstructured datasets at scale. Ensure data quality, reliability, and efficient processing across the ML lifecycle.Production Deployment & MonitoringDeploy machine learning models into production environments. Monitor performance, availability, and model effectiveness over time. Implement processes to support model retraining and continuous improvement.Experimentation & OptimisationDesign and analyse A/B tests and offline experiments. Measure model effectiveness and user outcomes. Use insights to drive ongoing optimisation and product improvements.Collaboration & InnovationPartner with Product, Engineering, Data Science, and Business teams to align machine learning initiatives with strategic goals. Stay up to date with emerging developments in machine learning, deep learning, and personalisation technologies. Identify opportunities to introduce innovative approaches and


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