I build practical AI systems that turn messy information into useful workflows: RAG apps, LLM agents, analytics dashboards, automation tools, and polished web experiences.
- AI agents and LLM tooling: browser agents, prompt workflows, document generation, and tool-using assistants.
- RAG and NLP systems: retrieval pipelines, question answering, text-to-speech experiments, and education-focused AI support.
- Data science products: predictive models, dashboards, competition notebooks, and business analysis projects.
- Full-stack applications: TypeScript/React platforms, FastAPI services, Python backends, and deployable prototypes.
- Advancing through the IIT Madras BS in Data Science & Applications program.
- Building production-ready AI engineer projects with Python, TypeScript, FastAPI, LLMs, and data pipelines.
- Turning academic projects, hackathon ideas, and experiments into clean public repositories.
- Strengthening ML fundamentals, RAG evaluation, agent design, and full-stack deployment.
- IIT Madras BS in Data Science & Applications: progressed through foundation and diploma-level coursework into the BSc track.
- Mathematics & Economics background: additional academic base for statistics, business analysis, and modeling.
- ML projects and competitions: flight-ticket price prediction, churn prediction, restaurant analysis, purchase-value modeling, Titanic analysis, and business data analysis.
- Build first, polish next, then document so others can actually use it.
- Prefer useful AI over flashy AI: measurable workflows, clear outputs, and practical deployment.
- Treat data quality, evaluation, and user experience as part of the engineering problem.
- Keep learning in public through projects, notebooks, and shipped prototypes.