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Swayam Patil

@swayampatil

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Full-stack and AI engineer building real-time, RAG-driven systems that automate support and improve reliability.

India

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What I'm looking for

I’m looking to build production AI features end-to-end—scalable APIs, real-time UX, and reliable ML/CV—within a team that values measurable impact, clean engineering, and quick iteration.

Full-Stack Engineer with hands-on experience building production-ready, real-time web platforms from the ground up. Currently working at GoParkEasy, where I design, build, and scale end-to-end parking booking systems used in real-world scenarios.

I specialize in Next.js, React, MongoDB, and Python, with strong experience in real-time workflows, payment integrations, and map-based discovery using Google Maps APIs. My work focuses on building reliable APIs, optimizing performance with caching and rate limiting, and delivering clean, responsive user experiences across roles. I enjoy owning features end-to-end — from frontend UX to backend logic — and collaborating closely with product and operations teams to ship fast without compromising scalability or code quality. I’m especially interested in AI-driven products, data-backed systems, and solving real-world problems at scale

Experience

Work history, roles, and key accomplishments

Current

Full-Stack & AI Engineer

Current

GoParkEasy

Nov 2025 - Present (7 months)

Built a RAG-based ParkBuddy AI chatbot (Gemini + MongoDB vector search) that resolved 60–70% of tier-1 support queries in under 2 seconds. Developed caching and async job processing to cut database load by 10–20% and manual processing errors by ~40%, and implemented real-time availability updates for 100+ concurrent users.

Python SQL Upstash Redis BullMQ WebSockets Next.js TypeScript React MongoDB AI

Full Stack Engineer Intern

GoParkEasy

Aug 2025 - Nov 2025 (3 months)

Architected a production-grade parking SaaS using Next.js, Node.js, and MongoDB, delivering 450+ REST APIs across a 51-collection schema with 5+ role-based access tiers. Built real-time availability updates with Redis Streams and Socket.io and deployed a QR check-in system, improving throughput by 15% by addressing MongoDB indexing bottlenecks.

Next.js Node MongoDB REST APIs Role Based Access Control (RBAC)REDIS Streams Socket.IO Puppeteer QR Check In Performance Optimization

Machine Learning Intern

Infosys Springboard

May 2024 - Jul 2024 (2 months)

Developed BikeWatch, a computer vision system using YOLOv5 and DeepSORT to detect helmet violations and triple-riding from live traffic streams, improving enforcement efficiency by 45%. Achieved 85% classification accuracy using IoU-based tracking and automated violation uploads to AWS S3, reducing data-handling overhead by 60%, while mentoring a 5-member intern cohort.