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Hello World! I'm Angad Singh.

I'm a Software Engineer with 4 years of experience in the Payments space.

  • šŸŒĀ  I hail from Pune, a wonderful little city in the state of Maharashtra, India.
  • šŸ–„ļøĀ  You can reach out to me over on LinkedIn.
  • āœ‰ļøĀ  I'm also just an email away at [email protected].
  • šŸš€Ā  I'm currently working on AeroSage!
  • šŸ§ Ā  I'm learning ReactJS, Kubernetes and ElasticSearch.
  • šŸ¤Ā  I'm open to collaborating on Java and Spring based open-source projects.
  • āš” Iā€™m currently contributing to Volante Studio, at Volante Technologies. My recent work involves automating the delivery of real-time payments artifacts into a SaaS environment.

Skills at a Glance

C++PythonJavaScriptJavaGitHTML5ReactCSS3NodeJSMongoDBMySQLLinuxDockerGoogle Cloud

CS is better when we work together. Let's get connected!

Skills, Projects and Data

Stats Projects
Top Languages
Programming Languages Frontend Development Backend Development Data Tools and Databases DevOps, Tools & Cloud Platforms
java
cplusplus
python
react
css3
html5
javascript
spring
nodejs
mongodb
mysql
pandas
scikit_learn
docker
kubernetes
aws
postman
bash
gcp
azure
git
linux

Angad Singh's Projects

aerosage icon aerosage

Backend system for AeroSage flight search engine, powered by Java, Spring Boot and MongoDB.

aerosageui icon aerosageui

Frontend interface for AeroSage flight search engine, built with React.

dataguardx icon dataguardx

A Windows tool to keep your data safe with automated backups and easy restoration.

ios-assessment icon ios-assessment

Speer Technologies take home assessment for candidate iOS developers

netpulse icon netpulse

Real-time network monitoring for Windows using Wireshark.

nirbhaya-app icon nirbhaya-app

Repository to hold Team Raksha's Nirbhaya App. #MOZOHACK19

pyrestomanager icon pyrestomanager

A restaurant management system created with Python and Tkinter.

timestamp-reranking-movie-recommender icon timestamp-reranking-movie-recommender

This project is a movie recommender engine written using Python and the Scikit-Surprise libraries to generate better movie recommendations by utilizing temporal user interactiondata.

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