Chetan Satkar

Designed and deployed an Agentic AI Interviewer platform using LangGraph, integrating multi-agent orchestration with adaptive difficulty logic and LLM-driven answer scoring, leveraging advanced prompt engineering to achieve consistent scoring behavior and structured outputs.

Developed a JEE-level Math Monitor ChatBot utilizing a Hybrid RAG pipeline (FAISS + HuggingFace embeddings) with direct LLM API integration, ensuring accurate and context-aware problem-solving capabilities.

Engineered LLM evaluation engines capable of generating structured JSON outputs, automating assessment and feedback processes, and significantly reducing manual review effort.

Applied advanced fine-tuning techniques (LoRA/QLORA/PEFT) on LLaMA models, enhancing model relevance and response quality for domain-specific hiring tasks.

Containerized and deployed AI agent systems using Docker and Kubernetes, ensuring reliable, scalable, and production-grade performance.

Developed an intelligent Event Booking Agentic AI System using LangChain, orchestrating autonomous agents with tool-calling and reasoning chains to automate multi-step booking workflows, exposed via a Flask REST API.

Engineered a full-stack Patient-Doctor Management System using Django, ensuring secure medical record handling, robust user authentication, and a scalable MVC-style backend architecture.

Designed and implemented robust REST APIs with Flask and FastAPI, facilitating seamless integration of medical data services with frontend and third-party platforms.

Constructed robust data models and implemented strong software engineering practices for secure and efficient management of sensitive patient and doctor records in production environments.

Integrated AWS services (Lambda, S3) to establish scalable storage and serverless workflows, ensuring reliable and cost-efficient deployment of critical applications.

Collaborated on developing model evaluation and data preprocessing pipelines using Python (Pandas, NumPy, Scikit-learn), directly supporting the implementation of AI-driven healthcare features.

Conducted comprehensive data analysis on Hospital ER datasets using Python (Pandas, NumPy) and SQL, extracting actionable insights to support informed healthcare decision-making.

Developed and rigorously evaluated ML models on real-world medical data, applying advanced preprocessing, feature engineering, and Scikit-learn techniques to significantly improve model building and performance.