Jublie AI - Semantic Book Recommendation Engine
Production AI system using vector embeddings and LLMs for conversational book discovery. Runs in production answering reader queries every day.
Intelligent Systems Architect with 6+ years of experience, now a Master's student in Embedded and Intelligent Systems at Halmstad University in Sweden, currently focused on Embedded AI and world models
Kabir Tamari is an Intelligent Systems Architect, which is a formal way of saying he loves taking messy, human problems and turning them into software that quietly runs itself. Every project starts with the business problem, not the tech stack. He breaks it into processes, finds the bottlenecks, automates them, then optimizes what's left. Over six-plus years that approach has carried real systems into production, ERPs running entire businesses and AI automations that quietly do the boring work, written mostly in Python with FastAPI, Pydantic AI, Django and C++. He is now doing a Master's in Embedded and Intelligent Systems in Sweden learning how intelligence works below the API down to the metal. He loves building random things purely for the joy of it. And like most conscious beings, he loves playing/listening to music, gaming, and sim racing (Dirt Rally 2.0 right now) to get his mind off and reset.
Production AI system using vector embeddings and LLMs for conversational book discovery. Runs in production answering reader queries every day.
Vision LLM pipeline using Gemini for automated book cataloging. Extracts structured data from images with RAG-based genre classification.
Research project training a Tacotron model to synthesize Nepali speech, learning Devanagari's quirks straight from the data.
Architected hub-and-spoke system managing 65,000+ SKUs across multiple branches with real-time inventory synchronization.
Master's in Embedded and Intelligent Systems at Halmstad University, Sweden - Specializing in AI robotics, embedded systems, and intelligent agents
Python, Django, FastAPI, React, Next.js, TypeScript, Go, PostgreSQL, Redis, Elasticsearch, MongoDB, Docker, Kubernetes, AWS, GCP, Cloudflare Workers, LangChain, OpenAI, Gemini, RAG Systems, Vector Databases (Qdrant, Pinecone), Computer Vision, OCR, Semantic Search, TTS Systems, Embedded Systems, Robotics, Real-time Systems, Edge AI, IoT