Hi there
My name is Shaurya, I’m an AI product builder and data scientist, currently pursuing my Master’s at Carnegie Mellon University. I have a passion for building intelligent, useful systems, from generative AI tools and RAG pipelines to real-world agentic applications.
Outside of tech, I’m deeply curious about human behavior, storytelling, and systems thinking, and I strive to create solutions that blend utility with user impact.
Below are a few projects I’ve built to learn, solve problems, and push boundaries, both individually and with teams.
Education
Carnegie Mellon University
Master of Information Systems Management
Business Intelligence and Data Analytics, 2024–2025
- AI Engineering
- Machine Learning
- Data Science
- Product Design
- Statistical Modeling
Chandigarh University
Bachelor of Engineering – Computer Science
AI & ML Specialization, 2018–2022
- Data Structures & Algorithms
- Software Engineering
- Web Development
- Python & SQL
Experience
Research Assistant – Carnegie Mellon University
May 2025 – Present
- Retrieval-Augmented Generation (RAG)
- AutoGen Framework
- Vector Databases
- Prompt Engineering
- Python
Working under faculty guidance on building a multi-agent RAG system for navigating legal and policy documents. Responsible for implementing document chunking, embedding generation, vector storage, and conversational agent workflows using Microsoft AutoGen.
Computing Consultant – Carnegie Mellon University
August 2025 – Present
February 2025 – May 2025
- Technical Troubleshooting
- User Support
- Computing Services
- Team Collaboration
Assist with the implementation of AI-driven tools to improve efficiency and user experience. Provide consulting and guidance on integrating AI solutions into computing workflows, ensuring alignment with student and faculty needs. Collaborate with Heinz Computing staff to maintain and enhance computing services.
Data Analyst – Carnegie Mellon University
February 2025 – May 2025
- Data Analysis
- Data Scraping
- Data Curation
- Scripting
- Python
Conducted exploratory and statistical analysis on faculty survey data to uncover patterns in course feedback, TA performance, and student experience.
Software Engineer – YMGrad
2022 – 2024
- SQL
- Backend Development
- Data Analysis
- Data Modeling
- Python
- REST APIs
Contributed to the backend architecture of YMGrad, a platform supporting international students. Focused on optimizing database queries, managing large-scale user data, and ensuring secure API performance.
Projects
EU Navigator
Multiagentic RAG Personal Learning Portal
- LangChain
- FAISS
- Streamlit
- Llama 3.1
- BAAI/BGE-M3
- RAGAS
- AI
- Legal Tech
EU Navigator is a sophisticated Multiagentic Retrieval-Augmented Generation (RAG) system designed as a Personal Learning Portal for exploring European Union tech, data, and AI legislation. The system integrates multiple specialized AI agents for planning, retrieval, synthesis, and review—enabling evidence-based legal analysis with verified citations.
Built using LangChain, FAISS, and Llama 3.1, it preserves EU legal structure through section-based chunking and semantic search. A Streamlit-based learning interface supports modular exploration, progress tracking, and reflection journaling. Evaluation was performed with the RAGAS framework to measure answer faithfulness and relevancy.
UCI Air Quality Prediction with MLFlow
- Python
- MLFlow
- XGBoost
- Docker
- Evidently AI
- EDA
- Model Monitoring
Developed a complete machine learning pipeline for predicting air quality using the UCI dataset. The project included experiment tracking with MLFlow, real-time data ingestion through Kafka, and a deployed prediction API containerized with Docker.
Built advanced monitoring dashboards with Evidently AI to detect data drift and performance degradation. Focused on reproducibility, modular ML design, and robust model serving for continuous deployment and evaluation.
Question Answering System using BERT & Azure ML
- Python
- BERT
- Azure ML
- Hugging Face
- Deep Learning
- API Deployment
Built a fine-tuned BERT-based question-answering system capable of matching user queries with relevant answer sentences from the SQuAD dataset. The system combined semantic similarity scoring, sentence embeddings, and Jaccard/TF-IDF baselines for benchmarking.
Deployed the model using Azure Machine Learning, optimizing the scoring pipeline for latency and accuracy. This project demonstrated full-stack ML deployment, including model fine-tuning, packaging, serving, and real-time inference.
Fairness Audit Playbook
Responsible AI Evaluation Framework
- AI Ethics
- Fairness Metrics
- Python
- Data Science
- Governance
- ML Evaluation
Created a comprehensive Fairness Audit Playbook that standardizes fairness evaluation for AI systems across domains. The framework integrates bias detection, intersectional fairness analysis, and historical context assessment to promote responsible AI adoption.
The playbook includes fairness definition selection, bias source identification, validation workflows, and implementation guidelines designed for engineering teams. Presented with case studies and visualization modules for explainability.
Contact
Reach out on
LinkedIn
or email me at
i.shauryagulati@gmail.com