Experience
Data Science & AI Intern
Jan 2026 – Aug 2026Carlisle Construction Materials, Pennsylvania
- ▸Developed an agentic AI system using LangGraph and Azure OpenAI to automate extraction and classification of construction product specifications from unstructured documents, reducing manual review time by 40%.
- ▸Built a multi-agent orchestration pipeline for intelligent document parsing, leveraging RAG with vector search to answer technical queries across 10,000+ product datasheets.
- ▸Designed and deployed a Streamlit-based internal tool for quality engineers to query product compliance data in natural language, increasing team productivity by 25%.
- ▸Implemented CI/CD pipelines with Docker and GitHub Actions for model versioning and deployment to Azure, ensuring reproducibility and scalability across environments.
AI Engineer Intern
Aug 2025 – Oct 2025Pegasus Knowledge Solution Inc.
- ▸Architected and deployed a production-grade RAG pipeline using LangChain, ChromaDB, and Azure OpenAI, enabling semantic search over 50,000+ enterprise knowledge articles with 92% retrieval accuracy.
- ▸Fine-tuned open-source LLMs (LLaMA 3, Mistral) on domain-specific corpora using LoRA and QLoRA, achieving a 15% improvement in task-specific response quality.
- ▸Built a multi-agent system using LangGraph for automated report generation, integrating tool-use agents with SQL databases and REST APIs for real-time data retrieval.
- ▸Developed evaluation frameworks using RAGAS and custom metrics to benchmark RAG pipeline performance, driving iterative improvements in chunking strategy and prompt design.
Data Science Intern
May 2025 – Aug 2025Van Brunt & Associates, Texas
- ▸Designed and deployed a modular machine learning pipeline to forecast daily and weekly peak loads in the ERCOT grid, achieving an 18% improvement in predictive accuracy.
- ▸Engineered robust feature sets by integrating ERCOT system forecasts, multi-source weather data, and tariff-based signals such as 4CP demand indicators.
- ▸Built a dual-objective model to simultaneously forecast load and identify 4CP events, attaining 87% precision and reducing commercial billing risk.
- ▸Implemented a scalable and reproducible pipeline using Python, SQL, and MLflow, enabling seamless experimentation and production deployment.
Senior Software Engineer
Aug 2019 – Mar 2024Infosys, Bangalore
- ▸Led development of ETL pipelines processing 2M+ daily records using Python and DB2 SQL, reducing data latency by 30% and enabling real-time dashboards for stakeholders.
- ▸Built predictive models for client churn analysis using Scikit-Learn and XGBoost, achieving 89% AUC and directly informing retention strategies that reduced churn by 12%.
- ▸Automated reporting workflows with Python and SQL, eliminating 20+ hours/week of manual effort and improving reporting accuracy by 35%.
- ▸Collaborated with cross-functional teams to design and maintain application programs for IBM i systems using RPGLE and CLLE, ensuring 99.9% system uptime.
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MS Business Analytics and AI
University of Texas at Dallas
GPA: 3.89August 2024 – May 2026 (Expected)
BE Computer Science
Nitte Meenakshi Institute of Technology
GPA: 3.65Jun 2015 – Aug 2019