Mayur Jadhav

Built end-to-end sales automation workflows, high-concurrency data pipelines, and optimized web infrastructure for location technology systems.

• Built end-to-end sales automation workflows using Python and Flask, streamlining lead capture and removing manual handoffs across internal tools.
• Architected a cache-backed write coalescing pipeline to aggregate bursty workloads and eliminate redundant writes, reducing database load by 98%.
• Improved Core Web Vitals by restructuring frontend architecture with modern tooling, achieving a 35% reduction in page load latency.
• Optimized PostgreSQL performance via query tuning and Prisma ORM improvements, maintaining low-latency access under rapid data growth.
• Developed type-safe REST APIs with Next.js and TypeScript to support high-frequency data updates across distributed services.
• Implemented RBAC, enabling role-scoped permissions and map-entities access for secure access to sensitive features and data.
• Owned features end-to-end from design to production, defining architecture and shipping iteratively in a fast-paced environment.

Architected low-latency RAG pipelines, high-concurrency GraphQL gateways, and offline-first data sync engines.

• Architected an optimized RAG pipeline and Node.js backend, reducing end-to-end API response latency by 40% for production LLM workloads.
• Developed a high-concurrency GraphQL gateway to streamline request routing and authentication; validated system stability under heavy load using Grafana k6.
• Engineered an offline-first data sync engine utilizing IndexedDB and Redux, enabling seamless manipulation of large-scale datasets with zero network dependency.
• Built a modular, flow-based frontend using ReactFlow and TailwindCSS, creating a highly extensible interface for complex data visualization and node-based logic.