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# Senior Machine Learning Engineer (Recommendation)

- **Company:** Sky UK
- **Location:** Grays, UK (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Big Data, Content Analysis, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Software Deployment, Data Streaming, Data Processing, Feature Engineering, Pytorch, System Availability, Deep Learning, Generative AI, Scikit Learn, Kubernetes, Machine Learning Operations, Data Pipelines
- **Published:** June 3, 2026
- **Apply:** https://find.jobs/jobs-near-me/senior-machine-learning-engineer-recommendation-grays-essex/2797209541-2/

## About the Role

* Demonstrated expertise in the full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance.
 * Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). .
 * Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton, TorchServe).
 * Experience with high-volume data processing and real-time streaming architectures.
 * Strong understanding of recommendation system design and personalisation algorithms.
 * Familiarity with Generative AI and its applications in production settings.
 * Exceptional communication and analytical problem-solving skills.
 * Proven successful experience in mentoring less experienced engineers to improve their technical skills

## Description

We are seeking a highly skilled Senior Machine Learning Engineer to advance our personalised recommendation systems by developing efficient, low-latency solutions that serve millions of users globally. The successful candidate will collaborate closely with data scientists, engineers, and product managers to design intelligent content recommendation mechanisms and drive the ongoing advancement of our Machine Learning Platform.

 * Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis.
 * Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets.
 * Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance.
 * Experimentation: Design and analysis of A/B tests and offline experiments to evaluate model efficacy and support continuous improvement.
 * Cross-Functional Collaboration: Engage with multidisciplinary teams to align machine learning initiatives with business objectives and user needs.
 * Research & Innovation: Evaluate emerging research in machine learning, deep learning, and personalisation for potential integration within existing systems.


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