On a mission to build the future of intelligent systems.
I’m an aspiring AI/ML engineer focused on machine learning, deep learning, computer vision, and applied AI systems. I enjoy learning by building and experimenting with real-world problems, while developing a deeper understanding of how AI systems work under the hood.
I’m particularly interested in intelligent systems, multimodal AI, computer vision, and local AI inference. I’m continuously exploring new ideas, building projects, and strengthening my fundamentals to become a better AI engineer and researcher.
An AI-powered surgical video analysis platform using Computer Vision, Vision-Language Models (MedGemma), and audio transcription. Built asynchronous Django REST APIs for video processing, job tracking, and report generation with FFmpeg, pHash, and HDBSCAN.
Built a real-time AI voice agent designed for clinics and small businesses using local LLM inference, speech-to-text, and voice synthesis.
A real-time face-swapping system using computer vision and deep learning techniques with facial landmarks alignment.
A web crawling and retrieval system that gathers news across AI, tech, and science, with embedding generation and retrieval-augmented generation (RAG).
Reimplemented AlexNet and ResNet from scratch in PyTorch to study deep learning architectures and training procedures.
Real-time QA app that blends web search with LLMs, using LangChain, Tavily for live results, and Google Gemini for intelligent answers.
Delivered ML solutions by building and deploying computer vision models, real-time voice agents, and deep learning architectures.
Dedicated one year to full-time ethical hacking, focusing on network security, penetration testing, and Linux systems. Participated in CTF challenges using Metasploit, Nmap, Wireshark, and Burp Suite.
University of Kerala
Specialization in Computer Hardware and Systems Maintenance.
A deep dive into Word2Vec from the ground up — exploring context windows, embeddings, and step-by-step backpropagation.
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A beginner-friendly guide exploring vectors, fundamental operations, and how to implement them intuitively in Python with NumPy.
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Learn how to train linear regression models without using gradient descent.
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