Overview
The Machine Learning Platform Engineer will play a critical role in enhancing the infrastructure that supports machine learning operations. This position involves close collaboration with Data Scientists and engineering teams to ensure seamless transition of machine learning models from experimentation to production. The primary focus will be on optimizing workflows, training pipelines, and platform capabilities essential for scalable machine learning implementation.
Responsibilities
- Design, build, and optimise machine learning workflows and supporting platform infrastructure.
- Develop scalable feature engineering pipelines using PySpark and modern data platform tooling.
- Build and maintain workflow orchestration frameworks for training, retraining, and inference workloads.
- Support experiment tracking, model lifecycle management, and reproducibility practices.
- Design and implement feature store capabilities and supporting data products.
- Work closely with Data Scientists to productionise models and improve operational reliability.
- Optimise distributed training and inference workflows, including performance tuning and resource utilisation.
- Contribute to platform architecture, observability, monitoring, and engineering standards.
Requirements
- Strong software, platform, or data engineering background.
- Experience working alongside Data Scientists and supporting machine learning solutions in production.
- Proficiency in Python and PySpark.
- Experience building and operating production data or machine learning platforms.
- Familiarity with workflow orchestration tools such as Airflow, Dagster, or Kubeflow.
- Knowledge of ML lifecycle tooling including MLflow or SageMaker.
- Understanding of feature engineering, feature stores, and model deployment monitoring.
- Strong grasp of distributed compute environments and production reliability.