Background

From an embedded lab bench
to research at IIT Bhilai.

I started out fascinated by the boundary where signals from sensors become decisions — a motor spins, a camera triggers, a robot reacts. That fascination became a career across embedded systems, computer vision and cloud platforms, and is now the foundation of my postgraduate research.

Over 4 years as a Senior Software Engineer at Infinity Tech Resources, I shipped production systems spanning machine learning pipelines, robotics integrations and cloud infrastructure. In parallel, I contributed to AI R&D for leukemia detection with the Peter Moss Leukemia AI Research Association, and worked on computer-vision and IoT projects with The Sparks Foundation.

Today, as an M.Tech Mechatronics researcher at IIT Bhilai, my work focuses on real-time industrial anomaly detection with edge-deployed YOLO models, and multi-modal LLM/RAG systems that turn raw sensor and vision data into human-readable plant intelligence. My 2022 SSRN publication on adaptive Kalman-filter object tracking with YOLOv5 sits at the centre of that interest — accurate perception that runs in real time, on real hardware.

Outside of research, I volunteer extensively with IEEE — currently Chair of the CEDA Maharashtra Section's Nagpur Chapter — and am an Intel Software Innovator and Arm Developer Ambassador. I maintain 500+ open-source repositories and enjoy turning what I learn into talks, workshops and mentorship for student developer communities.

02 — Toolkit

A stack built for hardware-aware AI.

From low-level embedded C to cloud-scale ML pipelines — the tools I reach for most, grouped by where they show up in a project.

01 Languages & Foundations

Python C++ C JavaScript MATLAB HTML5 CSS3

02 AI & Machine Learning

PyTorch TensorFlow OpenCV YOLOv5–v8 Scikit-learn Hugging Face LangChain / RAG

03 Edge AI & Robotics

OpenVINO ROS / ROS2 Arduino Raspberry Pi Jetson Nano Kalman Filters Simulink

04 Cloud & DevOps

AWS Azure GCP Docker Kubernetes Git / GitHub CI/CD

05 Web & Tooling

React Node.js Bootstrap REST APIs Linux VS Code
03 — Working Principles

How I approach a problem.

01

Hardware-first intuition

I prototype against real sensors, cameras and actuators early — simulations are useful, but the messy edge cases live in hardware.

02

Production over notebooks

A model isn't done at 95% validation accuracy — it's done when it runs at target FPS on the actual deployment hardware.

03

Evidence-based iteration

Every claim — 98% training accuracy, 45 FPS at 1080p — is backed by a documented experiment, the same rigor as my published research.

04

Teach as you build

IEEE talks, mentoring and 500+ open-source repos turn a solo project into something the next person can learn from.

04 — Beyond Engineering

Building communities, not just systems.

A parallel track of recognition and volunteering — the full list of roles and organisations lives on the experience page.

Intel Software Innovator

Recognised for AI & edge-computing projects built on Intel hardware and toolchains like OpenVINO.

Arm Developer Ambassador

Champion Arm-based development for embedded and edge AI within developer communities.

IEEE CEDA Chair, Nagpur Chapter

Leading IEEE CEDA initiatives and developer outreach across the Maharashtra Section's Nagpur Chapter.

500+ Open-Source Repos

A public body of work spanning AI experiments, robotics code, dev tooling and education resources.

Let's Build

Want the full picture?

Browse my projects, dig into the research, or just say hello — I read every message.