Team - Einstein Platform API (Platform for Native Machine Learning Application within Salesforce) • Worked with the backend team that owned the abstraction of the entities that store metadata adopted by almost all native AI applications in Einstein Platform like Predictions, Recommendation Builder, Case Classification to store the metadata about the application like data segments used for training, entities & fields used, prediction values, training metrics, prediction metrics along with storing authentication status, license management and user permissions using the java salesforce core framework. • Migrated to the currently adopted coherent and standardized data schema that was previously fragmented because of different acquisitions of machine learning platforms. Exposed parts of model management and training metrics metadata schema via Metadata and Tooling APIs with user permissions and licenses specifically along with enabling packaging for customers and vendors. Migrating the data schema helped in improving performance, usability, standardization and adoption scaling to billions of predictions • Effected a live migration to a unified platform as a part of multi team effort saving several million dollars per month in AWS costs. The framework designed with the team was adopted by the Einstein Apps independently with tooling, status transition, logs and dashboards incorporated for triage of issues. Specifically worked on the main tool used for triggering migration and entities that record the status with several stages and transitions available for rollback, monitoring and triaging scaling to millions of records. • Worked on Data Cloud metadata entities (DMO, DLOs Data Streams) that are used to provision an org to read data for training across different regions in AWS. Skills: Spring Framework · Java · Amazon Web Services (AWS) · Software Design · Machine Learning Platforms