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Nawazish Hassan

@nawazishhassan

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Computer Science student focused on GenAI, MERN full‑stack, and frontend engineering.

India

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What I'm looking for

I seek a hands-on role building production GenAI and web applications at a collaborative company that values rapid iteration, mentorship, and measurable impact.

I am a Computer Science student at IIIT Bhagalpur specializing in GenAI (RAG), MERN full‑stack development, and frontend engineering. I have deployed multiple production-grade applications and solved over 1000 algorithmic challenges, demonstrating strong system design and data-structure skills.

In professional projects I architected a RAG pipeline using Gemini API and vector embeddings, cut hallucinations by 40%, and optimized retrieval latency to under 100ms. I built a clinical management SaaS serving 300+ active users, improved MongoDB query performance by 35%, and automated prescription workflows to reduce manual effort by 60%.

As a leader and mentor I serve as Secretary & Technical Lead of my college Web Development Club, guiding 50+ juniors and running a 200+ participant hackathon. I seek to contribute reliable, performant systems and ship user-focused products leveraging modern AI and web stacks.

Experience

Work history, roles, and key accomplishments

Frontend Developer

CineVelo

Built a Netflix-style movie discovery frontend integrating TMDB API for 10,000+ titles, improved frontend performance by 25% with lazy loading and modular state, and maintained Firebase-backed watchlist persistence at 100% uptime.

TMDB API Firebase Firestore Vite Lazy Loading State Management UI Development

GenAI Developer

SymptoSense AI

Architected a RAG pipeline using Gemini API and vector embeddings that reduced hallucination rates by 40% and optimized retrieval latency to <100ms for 38 disease categories, while delivering a responsive medical UI and secure session analytics.

Gemini API Vector Databases Next.js MongoDB Atlas Performance Optimization

Full Stack Developer

DocuScript

Developed an E-prescription MERN SaaS used by 300+ active users, improved query performance by 35% via MongoDB indexing and bulk seeding, and automated prescription workflows reducing report generation time by 60%.