🎓 Master’s in Information Systems — Northeastern University, Boston
🔍 Actively seeking full-time roles in Data Engineering, Analytics, and AI Systems
I’m a data enthusiast with experience building end-to-end data pipelines, analytics platforms, and AI-driven systems.
My work spans data engineering, cloud platforms, reinforcement learning, and agentic AI, with applications across supply chain, finance, and healthcare.
I focus on designing systems that go from raw data → validated models → intelligent decision layers, with an emphasis on optimization, automation, and real-world impact.
- Built an agentic AI system using CrewAI with specialized agents for analysis, summarization, and task execution
- Implemented RAG pipelines with vector search for contextual CRM insights
- Automated workflows and alerts using n8n, with backend services in FastAPI
- Designed ELT pipelines in Snowflake + dbt to process 100K+ course records
- Applied RAG + NLP to map resumes to skill gaps, reducing course discovery time by 50%
- Built a Streamlit UI + LLM chatbot, improving learning relevance by 35%
- Developed PPO & DQN-based RL agents for multi-warehouse inventory replenishment
- Achieved 98.8% service levels while reducing inventory costs by 48%
- Visualized policy behavior and warehouse dynamics using Streamlit dashboards
- Built an end-to-end ETL and analytics pipeline using Talend, SQL Server, and Python for Chicago and Dallas inspection data
- Designed a star-schema dimensional model and validated inspection outcomes, risk categories, and violations
- Delivered Tableau and Power BI dashboards highlighting pass/fail rates, high-risk facilities, and geographic trends
- Processed 2.4M+ traffic collision records using Talend and Python
- Designed a dimensional data model in ER Studio to enable analytical querying
- Built geospatial dashboards in Tableau and Power BI to analyze crash severity and trends across U.S. cities
- Built a medical RAG assistant using Pinecone and LlamaIndex
- Indexed 27K+ WHO/CDC documents with 94% retrieval accuracy
- Integrated ML-based health risk prediction models with 87%+ accuracy
📄 Stock Market Prediction Using ML Techniques
Published in IJSART, Volume 6, Issue 1 — Jan 2020