Red Hat Technical Marketing Manger Internship

May - August 2025

From creating a RAG ChatBot to data analysis to a data graph recommendation system…

Broadly I…

  • Led end-to-end development of an AI chatbot for the Red Hat Architecture Center using LangGraph, Python, and Cursor. The chatbot was built on a Retrieval-Augmented Generation (RAG) framework to improve content discovery and user experience.

  • Built a proof-of-concept recommendation system using Python, machine learning, and Neo4j to model relationships between users, assets, and provisions within a graph database.

  • Designed an internal AI adoption framework to assess and benchmark team-wide AI efficiency. Conducted and synthesized insights from 36 internal interviews across the team.

  • Performed data exploration and analysis using SQL and RStudio to uncover user behavior patterns, asset utilization trends, and engagement metrics across the Red Hat Demo Platform.

The RAG ChatBot

  • built with the help of Cursor, Gemini API key, and LangChain

  • Advanced Question Type Detection — demo request, use case discovery, best practices, comparison, how to guides

  • Sophisticated Document Analysis — Document type classification, business relevance, target audience

  • Conversational Intelligence — Session management, conversational references, follow up detection

  • Natural Language Processing — Spell checks, entity recognition, business context extraction

I learned how to…

  • Translate technical lingo into easy to understand language to non technical people

  • Think like the user in a sense of product development and feature enhancement

  • Solve problems using automation and AI

  • Enable and integrate AI into daily workflows to increase efficiency and outcome

  • The iterative process of product development, trial end error is important