- Define success metrics, create baselines, and drive projects from prototype to production.
- Build and improve models spanning classification, information extraction, entity resolution, and anomaly detection.
- Design and evaluate prompt, retrieval, and tool-calling pipelines.
- Define datasets, labeling strategies, and data quality checks.
- Design offline evaluations and online experiments, and build monitoring for model performance.
- Build and operate training/inference pipelines, model serving, and feature pipelines.
- Collaborate with engineering to ensure scalability, reliability, and cost-effectiveness of ML systems.
- Work with product and customer-facing teams to translate business requirements into technical deliverables.
PythonSQLMachine Learning+5 more