כאן תמצאו את ג׳וב החלומות שלכם

Partner with MLEs in Data Science product teams and key stakeholders to design and maintain infrastructure for:
Data wrangling – supporting and enabling data requirements for research, training, validation, and testing.
End-to-end ML delivery – enabling model performance development, training, validation, testing, and version control.
Drive engineering best practices including code and model versioning, CI/CD pipelines, rollout strategies, and disaster recovery procedures.
Build and support monitoring and observability tools – dashboards, alerts, and performance tracking of models in production.