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# Machine Learning Engineer

- **Company:** Experis
- **Location:** Isleworth, 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 22, 2026
- **Apply:** https://www.experis.co.uk/job/machine-learning-engineer-6034917/apply

## About the Role

* Strong 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 Skills

 * Experience 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.

## Description

Machine Learning Development

 * Design, build, train, and optimise machine learning models focused on personalisation and recommendation systems.
 * Develop solutions covering:
 + Recommendation Engines
 + Ranking Algorithms
 + User Segmentation
 + Content Analysis
 * Evaluate and improve model accuracy, performance, and scalability., * Develop 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 & Monitoring

 * Deploy machine learning models into production environments.
 * Monitor performance, availability, and model effectiveness over time.
 * Implement processes to support model retraining and continuous improvement.

Experimentation & Optimisation

 * Design and analyse A/B tests and offline experiments.
 * Measure model effectiveness and user outcomes.
 * Use insights to drive ongoing optimisation and product improvements.

Collaboration & Innovation

 * Partner 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 improve existing solutions.


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