The intersection of research curiosity and engineering pragmatism.
I'm a software developer and AI engineer experienced in building complete machine learning pipelines — from dataset preparation to scalable deployment. I've worked extensively with deep learning frameworks like PyTorch and TensorFlow, and have built real-time applications using computer vision and transformer architectures.
My projects focus on solving practical problems such as accessibility (sign language translation), security (deepfake detection), and automated evaluation (GenAI grading systems). I emphasize reproducibility, scalability, and reliability using Docker, Kubernetes, and cloud workflows.
Beyond coding, I actively participate in hackathons and technical communities, where I also contribute as a technical lead and event organizer. I believe that great AI isn't just about accuracy scores — it's about building systems that are robust, deployable, and genuinely useful.
Real systems I've designed, built, and shipped. Not tutorials — solutions.
Live hand-gesture recognition and text translation for accessibility
Most sign language systems work offline or are too slow — making real-time communication difficult for deaf and hard-of-hearing users.
Biometric-level deepfake detection through iris texture patterns
Face deepfakes can bypass traditional detection systems, but iris texture is extremely hard to fake accurately — making it a strong biometric signal.
AI-powered answer evaluation using semantic understanding
Manual grading is slow, inconsistent, and non-scalable — especially for subjective or open-ended exam answers.
Production-ready ML lifecycle with containerized deployment
ML experiments are often unreproducible, and moving models from notebooks to production is slow and error-prone.
Where I've contributed, what I've built, and what I've learned.
VIT-AP University
Leading the development team of the university's technical club, organizing hackathons, workshops, and community tech events.
Self-directed Research & Development
Built multiple deep learning systems focused on real-time inference, computer vision, and LLM-powered applications with production-grade deployment.
Google Cloud x VIT-AP
Competed in a university-level hackathon sponsored by Google Cloud, building and presenting a cloud-native AI solution under tight time constraints.
VIT-AP University
Pursuing Computer Science and Engineering with a specialization in Artificial Intelligence and Machine Learning. Active in hackathons, technical clubs, and independent research projects.
Let's connect. Whether it's about AI, a role, or an idea — I'm always up for a conversation.