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Divya User

@divyauser26

AI engineer building agentic RAG systems and production-style GenAI applications, including a citation-grounded AI news agent.

India

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At Trident Group, I built and trained an XGBoost and Random Forest classification model for towel quality inspection. Using features engineered from a manually labeled image dataset, it reached 85% held-out test accuracy and replaced a six-person manual process.

I built an AI News Agent with a two-step agentic RAG pipeline using Gemini 2.5 Flash and Tavily search. It grounds each claim in a clickable citation, drops uncited claims, and includes a pytest suite for mocked LLM and search calls.

For the Google Cloud GenAI Hackathon, I built five applications with Vertex AI, Gemini, and Imagen, earning a Top 10 finish among 185,000 teams in Gold League, Phase 1. I also built Logbook, a Next.js and Supabase platform with Gemini API integration, and a LinkedIn posting agent with a human approval step.

Experience

Work history, roles, and key accomplishments

Engineering Intern

Trident Group

Jun 2026 - Aug 2026 (2 months)

Built and trained XGBoost and Random Forest classification models for towel quality inspection, reaching 85% held-out test accuracy and replacing a six-person manual process to save approximately ₹6,48,000 per month.

XGBoost Random Forest Feature Engineering Classification Python

Education

Degrees, certifications, and relevant coursework

Thapar Institute of Engineering & Technology

Bachelor of Engineering, Robotics and Artificial Intelligence

2023 -

Grade: 8.89/10

Activities and societies: Merit Scholarship of ₹2,00,000 awarded for academic excellence; relevant coursework includes Data Structures, Python Programming, Artificial Intelligence, Machine Learning, Applied Artificial Intelligence, and Data Science.

Pursuing a Bachelor of Engineering in Robotics and Artificial Intelligence, with a CGPA of 8.89/10 through Semester VI and a merit scholarship for academic excellence.