Senior AI / ML Engineer

What you'll bringDemonstrated expertise in the full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance.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.Good communication and analytical problem-solving skil ls. Good to have: Experience working on OTT platformsExperience in S cala

Senior AI / ML Engineer12 Month FTCWest LondonHybrid - Onsite 2 days per week Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a 'Cool Vendor' and a 'Vendor to Watch' by Gartner . What you'll be doingModel 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: Lead the 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 system