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.