nka0@home: ~

$ whoami

NKA0

Nagendra Shivasai Kanneboina // "Shiva"

> Applied CS student·data engineer·builder_

Available for Internships · Summer 2026

CS @ George Mason. I build AI-powered apps and the data pipelines behind them — from Python ML and ETL migrations to React frontends. I ship real things, mostly at hackathons and on caffeine.

// human, est. 2005 – 2135

./see-my-workresume.pdfcontact
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01.about

I'm Shiva — a Computer Science student at George Mason University (2023–2027) who builds AI-powered applications and the data engineering that makes them work.

I've interned as a data engineer migrating enterprise ETL systems, shipped ML frameworks at hackathons, and worked the full stack — from Python ML pipelines and computer vision to React frontends. My focus sits at the intersection of AI/ML, data engineering, and automation.

I like turning messy, real-world data into things that actually ship. Usually fast. Usually at a hackathon.

~/github/shivanka0 — live
14
public repos
building
$ top languages
HTML 30%Swift 20%TypeScript 20%C 10%Python 10%

02.experience

ETL Intern @ Prak Technologies

Sep 2025 – Dec 2025
  • 01Maintained and optimized Oracle & Teradata databases holding ~700k Medicare/Medicaid member records.
  • 02Led UAT for a Databricks migration and built Azure Data Factory pipelines replacing legacy IBM DataStage jobs.
  • 03Rewrote complex DataStage transformations using ADF expressions, SQL, and Databricks.
  • 04Ran row- and column-level reconciliation and supported the production cutover.

03.projects

startupEarly-stage startup★ featured

/PropScan

An AI-assisted property inspection platform I help build at an early-stage startup. I work on video-to-frame pipelines and room detection that feed a structured JSON schema, producing room-type reports — Python automation end to end, backed by MongoDB.

$Python$Computer Vision$FFmpeg$MongoDB$JSON Schema$AI
projectMedicare commission validation★ featured

/AI-Driven Anomaly Detection

An ML framework that flags irregularities in Medicare broker commission payments. I built an end-to-end pipeline in Python and SQL using Isolation Forest and feature engineering to automate risk scoring that previously relied on manual rule-based validation, with Power BI dashboards surfacing anomaly trends and commission variance.

$Python$Scikit-learn$Isolation Forest$SQL$Power BI$Feature Engineering
hackathonHopHacks

/Economic Data Prediction Platform

A platform that analyzes global economic data to forecast GDP, inflation, trade, and market stability. RAG pipelines ground LLM outputs in verified datasets, paired with time-series models, dashboards, and a chatbot/voice-bot interface for educational delivery.

$Python$RAG$LLM APIs$Time Series$Forecasting$Cloud
hackathonPatriotHacks

/AI-Powered NoteStream

An intelligent note-curation platform that aggregates, organizes, and summarizes user content into a living knowledge hub. AI-driven personalization, adaptive learning paths, and vector-DB search powered by RAG pipelines.

$Python$React$Node.js$Vector DB$RAG$AWS$Azure$GCP
more on GitHub →

04.stack

~/languages
$Python$Java$C/C++$JavaScript$TypeScript$SQL$HTML/CSS$Dart$Swift
~/frameworks + web
$React$Next.js$Node.js$Express$Tailwind CSS
~/ai + ml
$Scikit-learn$OpenAI APIs$RAG Pipelines$Vector DBs$Isolation Forest$Copilot Studio
~/data + cloud
$Azure ADF$Databricks$PySpark$Power BI$AWS$Oracle$MongoDB$Teradata$Snowflake
~/tools
$Git$GitHub$Vercel$FFmpeg$Jupyter$UNIX scripting$Autosys

05.certifications

Foundry & AIP Builder Foundations

Palantir Technologies · 2025

Machine Learning Foundations

AWS · 2025

$ ./say-hello

Let's build something.

Recruiting, collaborating, or just post-hackathon hello — my inbox is open.

open a channel →