Rahul Sharma

Full-Stack AI Engineer

Pune, Maharashtra

Profile Score

About

Full-Stack AI Engineer with hands-on experience designing and delivering production-ready web and mobile applications using React.js, Next.js, MERN stack architecture, and React Native. Experienced in building scalable product features and developing complex workflows, including a vendor-based cab booking platform with optimized UI/UX and robust validation systems. Implemented AI-driven semantic search using vector databases and Retrieval-Augmented Generation (RAG) to improve booking discovery and search relevance. Strong expertise in JavaScript, TypeScript, modern state management, and backend development using Node.js, Express, MongoDB, and MySQL. Skilled in building scalable frontend architectures with React and Next.js while integrating intelligent systems using vector databases, semantic search, and AI APIs. Passionate about developing high-performance, intelligent applications and leveraging full-stack and AI capabilities to build scalable, user-centric products in agile, product-driven environments.

Skills & Expertise (24)

Python Intermediate

7.8/10

Years Exp

React Intermediate

7.6/10

Years Exp

React Native Intermediate

7.4/10

Years Exp

FastAPI Netlify Vercel vs-code Jira Git LangGraph LangChain RAG VectorDB agentic AI MySql MongoDb Express.js Node.js CSS HTML Zustand REDUX Tailwind CSS Next.js

Work Experience

Full Stack Developer

The Return Journey

Jan 2025 - Jan 2026

Developed and maintained B2B and B2C web and mobile applications using React.js and React Native, serving internal teams and external vendors, improving platform responsiveness by ~30% and reducing UI load times through component optimization and efficient state management. Engineered core frontend modules including end-to-end booking workflows, Recent Searches, History tracking, and Razorpay payment integration (full and partial payments), increasing successful booking completion rate by ~25% and reducing manual booking intervention by ~40%. Designed and implemented a cab booking and vehicle onboarding system from scratch, handling booking lifecycle validations and vendor onboarding flows, improving booking accuracy by ~35% and reducing booking-related operational errors by ~30%. Integrated semantic search and vector-based retrieval (RAG) using Python and open-source embedding libraries, improving search relevance by ~40% and reducing average user search time by ~25% during tour discovery and booking workflows.