Data Engineer • Cloud Analytics Professional • Tech Enthusiast

• 🎓 Graduated CS Major at Northeastern University.
• 🌟 Skilled in Building scalable pipelines,Parallelizing DL models using HPC,Agentic Applications,etc.
• 🏆 Certified AWS & Azure Data Engineer Associate.
• ⚡ Fun Fact: I hit gym and cook to wind down
• 🌱 What I'm Up To: Currently exploring programming patterns and language agnostic solutions

1️⃣ Programming Languages

2️⃣ ETL Tools & Distributed Systems

3️⃣ Databases

4️⃣ Machine Learning Models

5️⃣ Data Modeling

Here’s a list of repositories from the BigDataTeam5 organization that can be included in your GitHub profile's README:

• Multiclassification-Off-road-terrain-using-Parallelization-Techniques Predicting smoothness of terrains with Images and Sensor readings using MLP and HPC clusters

• master-financial-database
  Repository for managing financial data with Python.

• AI-Info-Extractor_Markdown_Viewer
  Forked project for extracting and visualizing AI-related information using markdown.

• Incremental DataPipeline using Snowflake
  Developed an efficient ETL pipeline with incremental loading capabilities using Snowflake.

• LiteLLM SummaryGenerator with Q&A
  Python-based project for summarization and question answering with LiteLLM.

• Building a RAG Pipeline with Airflow
  Implemented RAG concepts to reduce input tokens in a language model pipeline.

• Nvidia-Agentic-Architecture-Workflow
  Built workflows to integrate agentic architectures with FastAPI and Streamlit.

• Multi-Agentic Hackathon Project
  A multi-agent system for crime analysis reports hosted on Streamlit.

• MarketScope AI-Powered Industry Segment Intelligence Platform
  A multifaceted application for healthcare vendors utilizing LangGraph and Airflow.

• Azure Spotify ML Pipeline
  Built scalable ETL pipelines and Random Forest models achieving an R² score of 0.82.
• Motor Vehicles Crash Analysis
  Analyzed crash data using Power BI and Talend, reducing traffic incidents by 35%.
• Kansas City Service Request Analysis
  Processed 1.56M service requests to optimize resource planning by 25%.