Responsibilities
* Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis.
* Construct and maintain robust, scalable data pipelines for feature engineering and model training using both structured and unstructured large‑scale datasets.
* Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance.
* Design and analyse A/B tests and offline experiments to evaluate model efficacy and support continuous improvement.
* Engage with multidisciplinary teams to align machine‑learning initiatives with business objectives and user needs.
* Evaluate emerging research in machine learning, deep learning, and personalisation for potential integration within existing systems.
Qualifications
* Demonstrated expertise in the full lifecycle of machine learning: model development, deployment, serving, monitoring, and maintenance.
* Strong proficiency in Python and experience with ML libraries/frameworks such as TensorFlow and PyTorch.
* Experience using ML training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and model‑serving technologies (e.g., 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, analytical problem‑solving skills, and proven experience mentoring less experienced engineers.
We are a Disability Confident Employer and welcome and encourage applications from all candidates.
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