I'm Prathmesh Chavan, focused on Artificial Intelligence, Machine Learning, backend engineering, and building practical software products. I enjoy turning ideas into working systems — from experimenting with ML and LLMs to building APIs, full-stack applications, and deployment pipelines.
I'm especially interested in the engineering side of AI: building reliable applications around LLMs, RAG, AI agents, model inference, computer vision, and applied machine learning. I like exploring how AI can be turned into useful developer tools and products that solve real problems.
I'm also passionate about open source and learning in public. A lot of my work lives in experiments, production-style projects, and contributions that help me understand systems more deeply.
- 🤖 I build applications around AI, ML, LLMs and AI agents
- 🧠 I enjoy working with RAG, deep learning, computer vision and model inference
- ⚙️ I build backend APIs, full-stack applications and production-style systems
- ☁️ I'm interested in cloud deployment, Docker, MLOps and scalable systems
- 🌍 I actively explore open source, developer tooling and new AI technologies
- 🚀 Currently open to software engineering, AI/ML opportunities and open-source collaboration
- ✍️ I learn by building, shipping, debugging, and improving real projects
- Artificial Intelligence & Machine Learning
- Large Language Models & AI Agents
- Retrieval-Augmented Generation (RAG)
- Computer Vision & Deep Learning
- Backend Engineering & APIs
- Full-Stack Product Development
- MLOps, Deployment & Cloud Infrastructure
- Open Source & Developer Tools
- Facial Recognition System — Computer vision project focused on face detection and recognition.
- DeepFake Detection — AI/ML project focused on detecting manipulated or synthetic media.
- InBox AleRt — A project focused on monitoring and handling inbox activity.
- AI Code Reviewer — AI-assisted code review and developer tooling project.
- Building production-ready AI applications
- LLMs, RAG and agentic systems
- Model inference and optimization
- Backend architecture and scalable APIs
- Cloud deployment and MLOps
- Open-source collaboration
