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Software Engineer · Texas A&M '26 · US Citizen

Hi, my name is NITISH. ELANGO.

I'm a full-stack & machine learning engineer from Dallas, TX. From distributed Java event streams to TensorFlow anomaly detection — I build systems that are fast, reliable, and actually work at scale.

About me

Let's build something ambitious.

I'm a Computer Science senior at Texas A&M University (Craig & Galen Brown Engineering Honors), graduating May 2026. I thrive at the intersection of backend engineering and applied machine learning.

At Charles Schwab, I worked on Java event streams processing $1.8B+ in daily mutual fund transactions. At the Department of Defense, I built anomaly detection systems that boosted situational awareness by 33%.

Whether it's migrating RabbitMQ to Kafka, building full-stack apps with OpenAI, or achieving a 34.49% ROI with predictive models — I like problems that actually matter.

Get in touch

$1.8B+

Daily transactions processed

33%

Situational awareness boost (DoD)

120+

Engineering hours saved

34%

ROI on ML prediction model

Experience

Where I've shipped things.

Jun–Aug 2025

Charles Schwab

Austin, TX

Software Engineering Intern — Back End Distributed Systems

Contributed to development, end-to-end testing, deployment, and CI/CD pipelines of Java event streams in Spring Cloud Data Flow processing $1.8B+ in daily mutual fund transactions.
Saved 120+ engineering hours and enabled release two sprints early by automating a Java Spring Boot 2.7→3.4 migration with GitHub Copilot and Gemini, validated via Splunk and JUnit.
Migrated services from RabbitMQ to Kafka by configuring headers and consumer groups, ensuring message reliability and system scalability.

Java Spring Boot Apache Kafka RabbitMQ Spring Cloud JUnit Splunk CI/CD

Jun–Aug 2024

Dept. of Defense

JBLM, WA · NSIN

Forward Deployed ML Engineer Intern — Anomaly Detection

Partnered with U.S. Army stakeholders in an Agile environment to deliver an Indo-Pacific anomaly detection product, navigating real operational ambiguity.
Boosted situational awareness 33% by deploying anomaly detection models (Clustering, LSTM) with TensorFlow, Keras, and Scikit-learn.
Increased model accuracy 20% via K-Means, DBSCAN, Linear Regression, hyperparameter tuning, and cross-validation.
Automated CSV-based data cleaning and preprocessing using Python and pandas.

TensorFlow Keras Scikit-learn Python Pandas LSTM K-Means DBSCAN

Projects

Things I've built.

React · Flask · AWS · OpenAI · MySQL

Insight Genie

Full-stack web app that lets users upload CSV files and ask natural language questions. Chart suggestions powered by the OpenAI API, deployed on AWS with PowerBI integration.

React · TypeScript · Express · PostgreSQL · Figma

Boba POS System

Prototyped a full POS system for a local boba tea shop — from Figma UI/UX to a REST API with Express and PostgreSQL, plus Google OAuth for secure session tracking.

TensorFlow · Python · Scikit-learn · Selenium

PROPSTER 34.49% ROI

Data science tool using Selenium for web scraping, TensorFlow and Scikit-learn for predictive modeling. Achieved a 34.49% ROI over 5 months with automated data pipelines.

Technical skills

My toolkit.

Languages

JavaPythonC++ TypeScriptJavaScriptSQL PostgreSQLC#R HTML / CSSLinux

Frameworks & Libraries

Spring BootTensorFlowPyTorch ReactAngularScikit-learn KerasPandasNumPy socket.ioNLTK

Infrastructure & Data

Apache KafkaRabbitMQ AWSDockerGCP MySQLMongoDBMaven Git / GitHubSelenium

AI Tools & Certs

GitHub CopilotOpenAI APIs Jupyter Notebook Kaggle ML CertPCEP (Python)