ML Systems Engineering: Design, develop, maintain, and optimize machine learning systems, including batch, real-time, and streaming inference patterns.
Design, build, and maintain ML infrastructure and tooling to support the full ML lifecycle (data preparation, training, evaluation, deployment, monitoring, and retraining).
Design, build, and maintain end-to-end machine learning pipelines on Databricks, covering data preparation, feature engineering, model training, evaluation, and deployment.
Own and extend the identity / firmographic and financial layers of the ICP Profile Schema, working from Companies House API data and the Data Platform.
Drive Real Impact: Lead an assigned project from inception to completion, applying your technical and analytical skills to solve a tangible business challenge (e.g., optimizing rider routing, scaling vendor acquisition).