Building RAG pipelines, agentic workflows, and ML-powered applications
Entry-level AI and Data Science engineer with a strong foundation in Python, SQL, and data analysis. I build full-stack, AI-powered applications using LLMs, retrieval-augmented generation (RAG), and agentic workflows.
| Education | B.Tech, Artificial Intelligence & Data Science, Dhanalakshmi College of Engineering & Science (CGPA 8.0) |
| Focus areas | Retrieval-Augmented Generation · Large Language Models · Agentic Workflows · Deep Sequential Learning |
End-to-end RAG system that analyzes and refines user queries with an LLM, then retrieves context from a ChromaDB vector store using hybrid semantic and keyword (BM25) search, merged with Reciprocal Rank Fusion and orchestrated through LangGraph.
Deep sequential learning system built on a stacked LSTM network that predicts player behavior patterns and churn risk, with an interactive Streamlit dashboard for real-time risk analysis.
Languages
AI / LLM
Data
Backend & Apps
Tools & Deployment
Open to entry-level roles in AI, machine learning, and data science. Reach me on LinkedIn