Led scalability and infrastructure planning efforts, including research and stakeholder discussions on storage architecture, CPU/RAM sizing, performance tradeoffs, and projected resource requirements.
Designed, developed, and enhanced a secure full-stack document management platform for a private government client, supporting scalable document ingestion, indexing, metadata tagging, and advanced search capabilities.
Evaluated and refined machine learning–based document classification systems by analyzing model outputs, assessing performance metrics, and informing iterative improvement strategies to improve categorization accuracy and downstream analytics.
Role Summary
Led scalability and infrastructure planning efforts, including research and stakeholder discussions on storage architecture, CPU/RAM sizing, performance tradeoffs, and projected resource requirements.
Designed, developed, and enhanced a secure full-stack document management platform for a private government client, supporting scalable document ingestion, indexing, metadata tagging, and advanced search capabilities.
Evaluated and refined machine learning–based document classification systems by analyzing model outputs, assessing performance metrics, and informing iterative improvement strategies to improve categorization accuracy and downstream analytics.
