Mayur Gajbhiye

Led end-to-end development of an Agentic RAG financial contract defect-scanner using FastAPI, LangGraph, ReactJS (Material UI), PostgreSQL and AWS, improving contract defect detection accuracy by 90% and reducing manual review effort by 5 hours. Contributed to productionizing a RAG-based AI pipeline and agentic workflows for extracting cashflow anomalies, incoterms, payment terms, and cross-country contract insights, improving analysis efficiency, data reliability, and explainability. Owned full-stack delivery for a team of 4 developers, leading backend architecture, React integration, CI/CD via GitHub, and AWS deployments to ensure smooth and reliable releases. Optimized PostgreSQL queries, API response handling, and frontend data flows for the AI application to improve performance, reduce API defects during UAT, and enhance overall user experience.

Led backend development of scalable Python/Django applications, designing RESTful APIs for authentication, payments, and data-driven platform features with a strong focus on reliability, maintainability, and performance. Built and optimized PostgreSQL database schemas, queries, and backend processing workflows to enhance application performance, data integrity, and system scalability. Enhanced backend systems for large-scale financial document processing by implementing structured extraction logic, optimizing SQL queries, and improving API orchestration to support accurate and efficient financial analysis. Deployed and maintained production applications on AWS EC2 using NGINX and Gunicorn, contributed to CI/CD pipelines via GitHub, and collaborated with DevOps teams to ensure infrastructure stability, performance, and reliable backend service operations.