About Me
Computer Vision and Edge AI Engineer | Robotics and Mechatronics, and community builder — I work where embedded hardware, computer vision and machine learning intersect, and I write about it for the people who'll build the next system.
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 graduate from 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.
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
02 AI & Machine Learning
03 Edge AI & Robotics
04 Cloud & DevOps
05 Web & Tooling
How I approach a problem.
Hardware-first intuition
I prototype against real sensors, cameras and actuators early — simulations are useful, but the messy edge cases live in hardware.
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.
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.
Teach as you build
IEEE talks, mentoring and 500+ open-source repos turn a solo project into something the next person can learn from.
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.
Want the full picture?
Browse my projects, dig into the research, or just say hello — I read every message.