I'm a Master's student at Carnegie Mellon University pursuing Information Systems Management with a focus on Business Intelligence & Data Analytics (GPA: 4.0/4.0). Previously, I worked as a Software Engineer at Salesforce, where I built automation pipelines, CI/CD systems, and AI-powered workflows.

I'm passionate about leveraging AI, data science, and automation to solve complex problems and create impactful solutions. Currently exploring LLM applications, agentic AI systems, and ML for real-world product innovation.

🔭 Currently working on: AI workflow orchestration & multi-agent systems
🌱 Learning: Advanced ML techniques, cloud architecture, and product strategy
💬 Ask me about: Python automation, AI agents, RAG systems, data pipelines, or Salesforce development
📫 Reach me: sumreenf@andrew.cmu.edu | Portfolio

Skills: RAG (Naive + Agentic) • LLM Fine-tuning • Prompt Engineering • AI Agents • Tool Calling • RAGAS Evaluation

Skills: CI/CD • Containerization • Cloud-native Development

Production RAG system that lets developers ask natural language questions about documentation and receive grounded, source-cited answers — powered by a local LLM running entirely on your machine.

• Tech Stack: Python, LangChain, LangGraph, Ollama (Mistral 7B), ChromaDB, SentenceTransformers, FastAPI, Streamlit, Docker, RAGAS
• Key Features:
  • Naive + Agentic RAG — two pipeline modes switchable via one env var. Naive retrieves once and generates. Agentic uses a ReAct agent that decides when and how to search, can perform multiple searches per question, and decomposes complex queries into sub-searches.
  • End-to-end pipeline: document ingestion → chunking → embedding → vector search → LLM answer with source citations
  • RAGAS evaluation: faithfulness 1.0, context precision 1.0, overall 0.819
  • 63 automated tests, Docker deployment, Streamlit chat UI with live health monitoring
• Impact: Zero hallucination (faithfulness 1.0) — every answer traces back to a specific document chunk
• 📘 Interactive Guide →

A distributed multi-agent AI system orchestrating 7+ autonomous agents with RAG pipelines and OAuth integrations.

• Tech Stack: Python, LangChain, OpenAI API, Google Workspace APIs, Vector Embeddings
• Key Features:
  • Event-driven workflow engine for Gmail, Calendar, and Notion integration
  • RAG-based knowledge retrieval with structured JSON outputs
  • Reduced manual productivity tasks by 80%
• Impact: Automated complex multi-step workflows across interconnected systems

Large-scale NLP analysis project for NVIDIA GTC positioning strategy.

• Tech Stack: Python, NLP, Sentiment Analysis, Tableau, Brandwatch
• Key Features:
  • Analyzed 100K+ social media conversations
  • Built sentiment analysis and topic modeling pipelines
  • Generated strategic recommendations for stakeholder presentations
• Impact: Identified perception gaps and emerging AI trends for product positioning

End-to-end ETL pipeline on real insurance claims data using Bronze/Silver/Gold medallion architecture.

• Tech Stack: Python, DuckDB, dbt, SQL, Tableau
• Key Features:
  • Built Bronze → Silver → Gold pipeline ingesting 1,338 healthcare claims with zero data loss
  • Kimball dimensional model with dim_patient and fact_claims tables using dbt
  • 6 automated dbt data quality tests passing across all transformation layers
  • 📱 Interactive HTML Report
• Impact: Identified smokers cost 3.8× more in claims ($32,108 vs $8,415 avg) through Gold layer analytics

AI-powered travel planning platform with real-time itinerary adaptation.

• Tech Stack: Python, LLM APIs, NLP, React, Weather APIs
• Key Features:
  • LLM tool-calling pipelines for dynamic travel recommendations
  • Processed 50K+ travel reviews for safety scoring and sentiment
  • Achieved 84% classification accuracy through A/B testing with 200+ users
• Impact: Personalized destination insights with real-time contextual signals

End-to-end demand forecasting pipeline for eyewear retail assortment optimization.

• Tech Stack: Python, XGBoost, Scikit-learn, Pandas, Time Series Analysis
• Key Features:
  • Built ML pipeline with 33 engineered features analyzing 910K+ units across 162 SKUs
  • Achieved 0.737 R² using XGBoost with hyperparameter tuning and cross-validation
  • Generated 14 data-driven product management recommendations
  • 📱 Interactive HTML Report
• Impact: Identified $2.3K/SKU revenue opportunity through strategic assortment reallocation

Carnegie Mellon University | Master of Information Systems Management (MISM)
Business Intelligence & Data Analytics | GPA: 4.0/4.0 | Expected Dec 2026

Certifications:

• 🏅 Salesforce AI Associate
• 🏅 Salesforce Advanced Admin & App Builder
• 🏅 Salesforce Advanced Developer & DevOps
• 🏅 Wharton School: AI for Business
• 🌟 Salesforce Ranger: 82,000+ Trailhead points

Software Engineer @ Salesforce (Jul 2023 - Aug 2025)
Built predictive analytics and automation workflows using Python, SQL, and APIs to optimize enterprise asset onboarding for 40K+ users.

Product Analytics & Insights Intern @ SRM Films (May 2023 - Jul 2023)
Analyzed engagement, retention, and churn metrics and developed dashboards to support data-driven content strategy.

Summer Analyst Intern @ Salesforce (May 2022 - Jul 2022)
Developed automation pipelines integrating Slack and MuleSoft APIs to streamline enterprise onboarding workflows.