Embedded Systems & Edge AI Engineer —
Building real-time IoT systems, digital twins, and TinyML solutions
that live at the intersection of hardware and intelligence.
I'm an Electronics & AI engineering student from Chalakudy, Kerala — obsessed with the point where firmware meets intelligence. From flashing microcontrollers at 3am to training models that run on 256KB of RAM, I build things that work in the real world.
My background spans mechanical fabrication (lathe, welding, CNC) and software systems (full-stack IoT, ML pipelines, WebSocket interfaces). I believe the best engineers are also craftspeople.
Currently pursuing B.Tech in CSE (AI) at Adi Shankara Institute of Engineering and Technology, while building projects that blur the line between hardware and software.
Digital Twin framework for precision farming. Builds a real-time edge-to-cloud IoT pipeline connecting ESP32 sensors to a Raspberry Pi hub and cloud analytics backend.
Smart Water Digital Twin system for ML-based pipe leak detection. Combines physical flow constraints with anomaly detection algorithms and live Streamlit dashboards.
WiFi-controlled 4×4×4 LED cube with a real-time WebSocket interface. Features a Three.js browser-based 3D simulator and LittleFS-hosted web UI — firmware on PlatformIO.
Embedded arcade game running on ESP32-S3. A retro space shooter with procedural enemy waves, power-ups, and particle effects — built entirely from scratch on bare metal.
AI banana ripeness analyzer using Groq's Llama 4 Scout vision API. Delivers variety-aware ripeness analysis with hilarious Malayalam-language roast feedback. Built for TinkerHub "Useless Projects" hackathon.
Modular robotics control framework built with PlatformIO and C++. Designed for scalable, reusable embedded robotics applications with clean hardware abstraction.
Custom Marlin 2.x firmware configuration for Creality Ender 3 Pro. Finely tuned for reliability and print quality with advanced features enabled.
Designing hardware that responds intelligently to its environment — from sensor fusion to cloud sync, making the physical world programmable.
Embedding intelligence into constrained devices — TinyML, edge inference, and neural networks running on kilobytes of RAM with real-world latency.
Soldering iron in hand, schematic on screen — building from scratch, debugging signals, and the satisfaction of hardware that actually works.
Technology that matters. Automation, monitoring, assistive devices — engineering with purpose, local context, and measurable impact.