Headline

AI Engineer | Aspiring Generative AI Specialist | Building RAG, LLM amp; Multi-Agent Systems | LangChain • LangGraph • CrewAI • Python | Open to AI/GenAI Opportunities

About

AI Engineer specializing in Generative AI, LLM applications, RAG, multi-agent systems, and applied machine learning. Hands-on experience building and deploying production-oriented AI solutions using LangGraph, LangChain, CrewAI, ChromaDB, GCP Vertex AI, FastAPI, Docker, and computer vision models. I completed AI Engineer training at ProITBridge, Bangalore, and have built an independent portfolio of end-to-end AI projects focused on real-world use cases: 1. Research RAG Agent — LangGraph orchestration, GCP Vertex AI embeddings, ChromaDB retrieval, and Groq inference, tested end-to-end on technical papers. 2. Multi-Agent Research Assistant — CrewAI-based multi-agent system with Planner, Retriever, Researcher, Verifier, and Writer agents, featuring ChromaDB RAG, claim-level verification, FastAPI backend, and Streamlit interface. 3. Invoice OCR Extractor — IBM watsonx.ai + Llama 3.2 Vision for automated document data extraction, built with FastAPI and Streamlit. 4. Hazardous Waste Detection System — YOLOv8 instance segmentation, deployed using FastAPI and Docker. 5. Bad Debt Prediction System — XGBoost/Random Forest-based credit risk modeling. Core skills: Python, LangGraph, LangChain, CrewAI, RAG, ChromaDB, FAISS, Hugging Face Transformers, GCP Vertex AI, Groq, FastAPI, Docker, XGBoost, YOLOv8. M.Sc. Chemistry with prior experience as a Research Chemist, followed by a transition into AI/ML through structured training and hands-on project development. Open to AI Engineer, GenAI Engineer, and Junior AI/ML Engineer opportunities in Hyderabad and Bangalore.

Location

Bengaluru

AI stack

claude code

github copilot

hugging face

langchain

langgraph

mcp

Work experience

AI Engineer - Internship at PROITBRIDGE, May 2025 - Present · 1 yr 6 mos, Bengaluru, AI Engineer Training — ProITBridge, Bangalore Completed intensive, hands-on AI Engineer training focused on Generative AI, LLM applications, RAG systems, NLP, and Machine Learning, with practical experience building end-to-end AI solutions. Key Contributions: • Built a RAG-powered Document Intelligence system using LangChain, FAISS, and Hugging Face Transformers, implementing document ingestion, semantic retrieval, context augmentation, and LLM-based question answering. • Designed LangGraph-based AI workflows for multi-step task orchestration, enabling structured execution of complex LLM-powered tasks. • Worked with LLM and NLP techniques, including embeddings, semantic search, prompt engineering, transformer-based models, and context-aware generation. • Developed and deployed interactive GenAI applications using Streamlit, integrating AI models with user-facing interfaces. • Built a Bad Debt Prediction system using Logistic Regression, XGBoost, and Random Forest for credit risk classification. • Developed a Movie Recommendation Engine using TF-IDF, Cosine Similarity, and Collaborative Filtering for personalized recommendations. Tech Stack: Python · Generative AI · LLMs · RAG · LangChain · LangGraph · FAISS · Hugging Face Transformers · NLP · XGBoost · Scikit-learn · Streamlit, AWS, GCP, AI agents, Crew AI, Auto Gen, Agentic AI, and Cloud Solutions.

Education

Master of Science - MS, Chemistry, Karpagam Academy of Higher Education

Public links

github: https://github.com/Sethupathi1105