Machine Learning Engineer

What you’ll be doing Model Development: Design, train, and optimise machine learning models for user personalisation, including recommendation systems, ranking models, user segmentation, and content understanding, with a strong focus on TensorFlow-based development. Data Pipeline Engineering: Build and maintain scalable data pipelines to support feature engineering and model training across large structured and unstructured datasets, leveraging cloud‑native tooling. Production Deployment: Deploy, monitor, and maintain ML models in production environments, including cloud‑based model serving on GCP. Ensure high availability, strong performance, and continuous model relevance. Experimentation: Lead A/B testing and offline experimentation to evaluate model performance and guide ongoing improvement. Cross‑Functional Collaboration: Work closely with engineering, product, data, and research teams to ensure ML solutions align with product and business goals. Research & Innovation: Stay informed on advances in machine learning, deep learning, and personalisation, and evaluate their integration into existing systems. What you'll bring • End‑to‑end experience across the ML lifecycle: model development, training, deployment, monitoring, and continuous maintenance. • Strong proficiency in Python and ML frameworks, with expertise in TensorFlow (and experience with PyTorch). • Experience with GCP machine learning and data services (e.g., Vertex AI, Dataflow, BigQuery, AI Platform, Pub/Sub). • Hands‑on experience with ML training frameworks such as TFX or Kubeflow Pipelines, and model‑serving technologies like TensorFlow Serving, Triton, or TorchServe. • Background working with large‑scale batch and real‑time data processing systems. • Strong understanding of recommender systems, ranking models, and personalisation algorithms. • Familiarity with Generative AI and its use in production environments. • Strong communication skills and analytical problem‑solving abilities.