Built and maintained Python/Flask backend services for indicative loan pricing (Fannie/Freddie) including property search, loan calculations, and Excel workflows
Improved API performance ~40% via Redis caching of frequently accessed loan/property data
Designed scalable file/export architecture using S3 pre-signed URLs and AWS Lambda async processing to avoid blocking API requests
Optimized PostgreSQL/SQLAlchemy performance with lazy loading, connection pooling, composite indexes, and bulk operations
Implemented read/write separation with PostgreSQL read replicas and SQLAlchemy routing at the repository layer
Integrated ML inference into backend via SageMaker endpoints with Celery/RabbitMQ async calls and Redis caching
Designed multi-tenant data isolation with tenant-ID enforcement, RBAC via Keycloak, and audit logging
Resolved production export failures (>50k rows) by redesigning to streaming + background processing; ~90% export improvement and >30% memory reduction
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