Lead Machine Learning Engineer

Key Responsibilities

• Build and mature ML deployment pipelines for enterprise model delivery.
• Develop ML pipelines using Kubeflow, TFX and Vertex AI.
• Establish robust model deployment, monitoring and operational support practices.
• Implement model preprocessing, optimisation, training and serialization workflows.
• Support A/B testing and continuous improvement of deployed models.
• Implement CI/CD/CT practices for machine learning models.
• Manage Container Registry and Artifact Registry for ML workloads.
• Implement code quality, coverage and static analysis using tools such as Pylint.
• Drive Continuous Machine Learning (CML) practices across model development and deployment.
• Work closely with Data Science and Process Innovation teams to improve ML delivery and ways of working.

Essential Skills

• Strong experience building ML pipelines using Kubeflow and/or TFX.
• Hands-on Vertex AI experience as an orchestration layer.
• Strong Python development skills.
• Proven experience with enterprise model deployment and monitoring.
• Understanding of the complete ML lifecycle: preprocessing, training, optimisation and serialization.
• Experience with A/B testing of ML models.
• Strong GCP experience.
• Experience implementing CI/CD/CT for ML models.
• Hands-on experience with Container Registry / Artifact Registry.
• Experience with code quality, coverage and static analysis tools such as Pylint.

Desirable

• DevSecOps experience.
• Continuous Machine Learning / CML experience.
• Data Science project experience.
• ML/Data Science or Python/DevOps certifications.
• Strong stakeholder and communication skills.