Building AI systems, intelligent agents, and software products that solve real-world problems.
- 🤖 AI Agents & Autonomous Workflows
- 🔎 Retrieval-Augmented Generation (RAG) Systems
- 🧠 Machine Learning & Deep Learning Applications
- 🌐 Full-Stack AI Products
- ⚡ Hackathon Prototypes & Rapid MVPs
Privacy-first healthcare assistant combining LLMs, RAG, and Federated Learning for intelligent medical recommendations.
Stack: Python, LangChain, FastAPI, MongoDB, Federated Learning
RAG-powered document intelligence system that extracts, understands, and queries invoices and bills using natural language.
Stack: Python, LangChain, FAISS, OpenAI
AI agent capable of researching products, navigating websites, and performing multi-step information gathering tasks.
Stack: Python, Selenium, LLMs, Agent Frameworks
Deep learning system for personalized music generation and audio intelligence.
Stack: PyTorch, TensorFlow, librosa, torchaudio
Core Areas
- LLMs
- RAG
- Prompt Engineering
- NLP
- Deep Learning
- Federated Learning
- Model Optimization
- Predictive Modeling
- Multi-Agent Systems
- Model Context Protocol (MCP)
- Advanced RAG Architectures
- Agent Memory Systems
- AI Product Engineering
- AI Engineer Internships
- Applied ML Roles
- AI Product Engineering Opportunities
- Hackathons & Open Source Collaborations
Technology is most valuable when it augments human capability, not replaces human judgment.
I'm interested in building trustworthy AI systems that solve meaningful problems in healthcare, knowledge retrieval, automation, and human productivity.
Outside of AI, I'm interested in storytelling, filmmaking, worldbuilding, psychology, and the long-term impact of technology on society.