Sanjana Waghmare

About

BS Data Science & Applications student at IIT Madras building LLM systems, including RAG chatbots and multi-agent research pipelines. Experience spans the ML lifecycle from data engineering and model benchmarking to deployment with Streamlit and FastAPI.

Education

• BS in Data Science & Applications
  IIT Madras
  Data Science & Applications · 2023 – 2027

Skills

• SQL <3 months
• FastAPI <3 months
• Python <3 months
• Prompt Engineering <3 months
• Feature Engineering <3 months
• Matplotlib <3 months
• NumPy <3 months
• Pandas <3 months
• Streamlit <3 months
• scikit-learn <3 months
• PyTorch <3 months
• TensorFlow <3 months
• LangChain <3 months
• OpenAI API <3 months
• Plotly <3 months
• Random Forest <3 months
• API Integration Understanding <3 months
• RAG <3 months
• Vector Databases <3 months
• LangGraph <3 months
• AI Agents <3 months
• MCP <3 months
• XGBoost <3 months
• Semantic Search <3 months
• Hugging Face Transformers <3 months
• Ragas <3 months
• Faiss <3 months
• Agentic AI Systems <3 months
• Model Benchmarking <3 months
• SMOTE <3 months
• Function Calling <3 months

Tools / apps / platforms

• ChromaDB <3 months
• Docker <3 months
• Git <3 months
• GitHub <3 months
• GitHub Actions <3 months

Projects

• COVID-19 Epidemic Time-Series Forecaster
  Facebook Prophet, Pandas, Plotly, Streamlit, Python

  Time-series forecasting project using WHO epidemiological data, interactive EDA, Prophet modelling, and a Streamlit application for scenario exploration.

• Customer Churn Prediction System
  Scikit-learn, XGBoost, Random Forest, SMOTE, Streamlit, Python

  Full-cycle churn classification pipeline on the IBM Telco dataset with preprocessing, model benchmarking, serialisation, and a Streamlit dashboard for business users.

• Multi-Agent Research Assistant
  LangGraph, LangChain, OpenAI API, Tool Calling, FAISS, Python

  Multi-agent research pipeline with a Supervisor Agent orchestrating web search, retrieval, and synthesis sub-agents using structured tool-calling and evaluation harnesses.

• RAG Document Intelligence Chatbot
  LangChain, FAISS, ChromaDB, OpenAI API, HuggingFace, Streamlit, Python

  Production-grade end-to-end RAG pipeline for PDF ingestion, semantic chunking, vector retrieval, and context-grounded answer generation; includes modular codebase, tests, and a public Streamlit demo.

⚽ Extracurricular activities

• Ignite 64 Global AI Hackathon 2026

  Selected from a competitive global applicant pool to build a GenAI-powered solution in a 48-hour sprint as part of a 4-member team.

• SBI Global Fintech Hackathon (GFC) 2026

  Built an LLM-powered fintech prototype using RAG and agentic tool-calling workflows for a real-world banking use case.

• IIT Delhi Global AI Safety Hackathon 2026

  Applied alignment and evaluation frameworks to stress-test LLM-based systems for robustness and reliability in a team setting.