Machine Learning Engineer, Recommendation

Responsibilities

Design, train, and optimise machine learning models focused on user personalisation, including recommendation engines, ranking algorithms, user segmentation, and content analysis. Build and maintain robust, scalable data pipelines for feature engineering and model training using structured and unstructured large-scale datasets. Deploy and monitor ML models in production environments, ensuring high availability, performance, and continued relevance. Lead the design and analysis of A/B tests and offline experiments to evaluate model efficacy and support continuous improvement. Collaborate with cross-functional teams to align machine learning initiatives with business objectives and user needs. Evaluate emerging research in machine learning, deep learning, and personalization for potential integration within existing systems. Demonstrate expertise across 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 (e.g., TensorFlow Serving, Triton, TorchServe). Experience with high-volume data processing and real-time streaming architectures. Strong understanding of recommendation system design and personalization algorithms. Familiarity with Generative AI and its applications in production settings. Strong communication and analytical problem-solving skills.

#J-18808-Ljbffr