I’m a CS undergrad at Manipal Academy of Higher Education and I spend most of my time building things that work at scale. I’ve built distributed message brokers that handle 100K+ messages per second, AI pipelines that catch their own mistakes, and real-time computer vision systems deployed in corporate environments.

My Path into Engineering

I got into tech because I genuinely wanted to know how things work under the hood. Not just using tools, but actually understanding them well enough to rebuild them from scratch. That curiosity is what pushed me to dig into consensus algorithms, build my own message brokers, and tear apart neural network architectures to see what makes them tick.

At Manipal, I’ve kept an 8.7 CGPA while focusing heavily on building real projects. Not coursework projects, but actual production systems with proper architecture, tests, and CI/CD pipelines.

Building Real Systems

I think the best way to actually learn distributed systems is to build one yourself. So I wrote a Kafka-inspired message broker from scratch in Go with Raft consensus, gRPC transport, log replication, and segment-based storage. No external libraries for the core. Just me reading the papers and implementing them.

On the AI/ML side, I built NeuralRAG, a self-correcting RAG pipeline that goes beyond basic retrieval. It validates its own outputs, catches hallucinations, and automatically reformulates queries when the answers aren’t good enough. I learned more from debugging the failure modes than from getting the happy path working.

Industry Experience

During my internship at Reliance Industries Limited in the Video Analytics Division, I built real-time occupancy detection systems using YOLOv11 and DeepSort, processing 10+ concurrent RTSP camera feeds at 60+ FPS. The most interesting part was figuring out multi-camera spatial deduplication to avoid double-counting people visible from overlapping camera angles.

I also built an Edge Vision Gateway, a privacy-preserving pipeline that processes video locally using OpenVINO-optimized pose estimation. The key idea is that zero raw footage ever leaves the device. Privacy wasn’t an afterthought here; it was the whole point of the architecture.

What I Care About

I’m most interested in problems where systems engineering meets machine learning. The kind of work where you need both algorithmic depth and solid infrastructure thinking. Whether it’s making a message broker fault-tolerant or squeezing an inference pipeline onto edge hardware, I like the challenge of making things work reliably when it matters.

Currently exploring: Distributed ML systems, edge AI optimization, and production ML pipelines.

Small Achievements and Certs

• 300+ problems solved on LeetCode & CodeForces
• Top 10 at Honeywell SDG Hackspace 2025
• Deep Learning Specialization (Coursera)
• Microsoft Azure AI Fundamentals certified