Engineered a production-ready RAG microservice using FastAPI, Gemma-3, and ChromaDB for vector based data retrieval, summarization, and contextual Q&A.

Implemented session-based chat history, for a seamless conversational interface.

Designed the system with modular architecture for easy scaling across multiple AI-driven features and smooth deployment.

Developed dynamic visit form builders with multi-template functionality and field validation for customizable patient forms.

Implemented a visit comparison module to track patient progress side-by-side, enabling nutritionists to detect trends and improve treatment accuracy.

Reduced redundant API requests by 40-45% by optimizing state management, improving dashboard load times and lowering server strain.

Built calendar-based appointment views for scheduling and follow-ups, ensuring cross-device responsive UI for consistent user experience.

Implemented role-based authentication with permissions for multiple user tiers, ensuring secure access to sensitive data.

Reduced database calls by 20-30% using MongoDB aggregation pipelines, caching, and query restructuring.

Built real-time dashboards for inventory, asset, schedule, and task management, allowing admins and operators to track tasks and manage resources efficiently.

Delivered responsive, multi-tenant dashboards for admins and on-ground operators.

Developed API workflows to auto-create follow-up appointments when submitting prescriptions with next visit details.

Built features to generate prescription PDFs from doctor-submitted forms and invoice PDFs for reception workflows.

Integrated WhatsApp chatbot communication via third party provider for sending notifications, confirmations, and prescription reminders.

Documented all API endpoints thoroughly, reducing development time by 20% and streamlining frontend-backend workflows.

Refactored backend modules to improve query performance, enhancing efficiency by 20-30% through aggregation pipeline optimization, indexing, and error handling.