Eureka!!
Profile | Navin
Paris, Fr
About
Hey! I'm Navin, and I build intelligent systems.
A few years ago, I was deep in the full-stack world like React, Node, databases, the whole thing. I loved shipping products and solving problems. But somewhere along the way, I realized the coolest problems weren't just about building fast software; they were about making software think.
So I made the leap into machine learning.
For the past 6 months, I've been obsessed with deep learning. I've trained models, fine-tuned transformers, and shipped ML systems to production. Not just theory—real, working systems that solve real problems.
Now I'm living at the intersection of both worlds. I know how to build systems that scale, AND I know how to train models that actually work.
You'll find my projects on GitHub—complete pipelines from data to deployment. No half-finished experiments, just solid ML work.
Let's build something intelligent together.
vnavinvenkat@gmail.com
Career
Work experiences and roles
Jan 2025 – Apr 2025
Built and maintained scalable REST APIs with Node.js, Express.js and TypeScript, with unit and integration testing (Jest). Migrated the frontend from React.js to Next.js (SSR/SSG), improving performance and SEO. Optimised PostgreSQL and MongoDB schemas to reduce latency on high-traffic endpoints. Set up CI/CD pipelines via GitHub Actions and deployed Docker services on AWS EC2 with production monitoring. Integrated AI tools including Copilot and LLM APIs for code generation, automated review and documentation.
Projects I Developed..
What I’ve Built
Production-grade RAG system that lets users upload any PDF and ask questions in plain English — every answer comes with an exact page citation and zero hallucination. Built with hybrid BM25 + vector retrieval, Cohere cross-encoder reranking, and a Ragas evaluation pipeline CI-gated in GitHub Actions. Faithfulness improved from 0.61 to 0.89 after reranking. Multi-user architecture with NextAuth Google and X OAuth. Phase 2 adds fully local inference via Ollama with zero data egress.
Multi-user collaborative coding platform with real-time synchronisation under 100ms via WebSockets (Socket.IO). Features live cursor tracking, secure room management, and syntax highlighting — built for remote pair programming and team coding sessions. Event-driven scalable architecture using React, Node.js, MongoDB and Tailwind CSS.
LLM-powered search engine with real-time data retrieval and streaming responses via OpenAI and Anthropic APIs. Delivers concise semantic results beyond keyword matching, with advanced cache optimisation achieving a 95% cache hit rate, serverless deployment on AWS Lambda, and optimised PostgreSQL queries reducing latency by 60%.
Subscription-based website monitoring platform with real-time uptime pings, automated downtime email alerts via NodeMailer, performance metrics dashboards, and tiered subscription plans. Built with Node.js, TypeScript, Bull Queue for distributed async job processing, PostgreSQL and Prisma ORM — deployed on AWS EC2 with GitHub Actions CI/CD and 99.9% uptime.
Education
EPITA, l'école des ingénieurs en intelligence informatique
Master of Science in Computer Science