Overview
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.
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.
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