I build production-grade AI systems and full-stack web applications. From RAG pipelines and LLM integrations to scalable backends and interfaces that feel considered.
Born in Shikaripura, raised between Shimoga and Davanagere, building in Bangalore.
I'm an engineer who builds things end to end — production AI systems, RAG pipelines, full-stack web applications. The kind of work where the screenshot and the source code agree with each other.
Computer Science was the degree. Shipping was the education. Every project taught me something the syllabus didn't — how to pick the right model, how to design a clean API, how to know when something is actually finished.
Today I work across the stack: React, Next.js, Python. I build with LLMs, vector databases, and computer vision. The tools change. The standard doesn't.
Focused on full-stack development, data structures, algorithms, system design, and scalable application architecture.
Physics, Chemistry, Mathematics, Computer Science — the years where curiosity about software became a decision.
Foundation years. Discipline, structure, and the first seeds of an interest in how computers work.
Engineered a production-ready e-commerce platform with React, Express, and MongoDB — delivering 5+ core features end to end. Designed 10+ REST APIs, optimized database queries, and shipped 8+ responsive pages.
Building full-stack applications with Next.js, React, and Python. Production AI systems, RAG pipelines, and computer vision projects.
Built a Python and Flask medical detection prototype. Optimized model performance and integrated a clean backend API around it.
Built responsive UI systems using React and Tailwind CSS with clean component architecture. First year of shipping real interfaces.
Applied generative AI systems, prompt structuring, and AI-driven workflows for real-world implementation.
Python fundamentals strengthened with AI-assisted development and structured problem-solving approaches.
Structured data handling, spreadsheet logic, and foundational data analysis techniques.
Computer vision fundamentals and image generation workflows using modern AI tools and services.
Facial recognition concepts, detection pipelines, and applied AI vision model usage.
Object detection systems, model evaluation basics, and applied image recognition techniques.
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