Third-year AI & ML student who ships full-stack products end to end — from LangGraph agents and RAG pipelines to production deployments. Currently hunting internships in AI/GenAI and backend engineering.
I'm a third-year B.Tech student in AI & Machine Learning at SRKR Engineering College, Bhimavaram. Roughly two years ago I taught myself full-stack development, and since then I've moved from building CRUD apps to designing agentic AI systems — LangGraph agents, RAG pipelines, and multi-step reasoning workflows wired into real products.
I care about shipping things that actually run in production, not just demos: auth flows that hold up, database schemas that don't fall over, and deployments that survive real users. I debug in public — most of my projects have a trail of real bugs found and fixed along the way.
Right now I'm sharpening my data structures & algorithms fundamentals alongside exploring new AI tooling, so I show up to interviews strong on both the systems I've built and the theory behind them.
A conversational trip-planning agent built on LangGraph with persistent memory — plans itineraries, handles multi-turn corrections, and survives adversarial prompt-injection attempts I tested against it myself.
Three-role platform (admin, club coordinators, students) with role-based access control governing event creation and registration. Coordinators create, update, and delete events with photos; students browse and register directly, with email verification and password reset via time-limited JWT tokens over Brevo.
Gamified environmental-education platform built as team lead for Smart India Hackathon 2025 — separate student, teacher, and school logins, an AI chatbot for environmental Q&A, and a points/leaderboard system for completed activities.
Full-stack registration platform with QR-code check-ins, automated email confirmations, and image uploads — built and deployed for the SRKR Coding Club web developer assessment.
Production admissions workflow with strict five-step sequential enforcement, WhatsApp status notifications, and IST-safe scheduling logic.
Document question-answering system with a retrieval pipeline tuned across TF-IDF, BM25, and dense embedding approaches, evaluated with RAGAS.
Agentic recommendation engine built for a live technical assessment — earned a Round 1 interview off the strength of the submission.
Open to internships in full-stack, backend, and AI/GenAI engineering. The fastest way to reach me is email.