Software Developer Engineer

Venkata Pratyush Kodavanti

admv1b@r.postjobfree.com Fairfax, VA(Open to Relocate) 571-***-**** LinkedIn GitHub Website

Summary

A Master’s student with technical experience in software development. A self-motivated individual with good analytical skills and a knack for problem-solving. A passionate learner with a natural curiosity to try new technologies. A self-starter with strong communication skills with a discipline to prioritize things and achievie goals.

Education

Master of Science in Computer Science: George Mason University (GPA – 3.7) May 2021

Related Coursework: Analysis of Algorithms, Discrete Mathematics, Component-Based Software Design, Software Engineering for the www, Data Structures in C.

Bachelor of Technology in Computer Science and Engineering: GITAM University (GPA – 3.25) April 2016

Work Experience

Graduate Teaching Assistant (George Mason University) Aug 2020 – Present

Collaborated with the instructor and 5 other TA’s to lead labs, taught 60+ students programming in JAVA.

Assistant Systems Engineer - Software Developer (Tata Consultancy Services) March 2017 – June 2018

Involved in the development design and testing of a web application of RESTful Web Services using Java and Spring framework (server side); that links the civil, mechanical, and electrical departments for a petroleum-based client, thereby reducing traffic by 25% on the main webpage.

Collaborated with Agile development team in analysis, design, development, and testing of web-service using Object Oriented Programming (OOP) in Java, Spring framework, and MVC (Model View Controller).

Written SQL queries and stored procedures for information retrieval, manipulation, and performing operations on a highly scalable database of (3TB) with JDBC and Hibernate at the back end.

Skills

Programming and Databases: Java (J2EE), Python, C, SQL, PL/SQL, No SQL.

Web technologies: HTML, CSS, JavaScript, Springboot, React js, Ajax, jQuery, Spring, Hibernate, MVC.

Cloud Computing & Tools: AWS (EC2, S3, Lambda), Docker, Git, Kubernetes, Tomcat, Visual Studio.

Projects

IPL dashboard: - Java, Spring Boot, Spring Batch, React, AWS, HSQL, HTML, CSS Grid, Docker

Created a full-stack web application using Java, and Spring boot that is used to keep track of the latest fixtures of each team as well as all the matches that have happened from 2008 to 2020 in the Indian Premier League.

Used Spring Boot for the main server application, Spring Batch to ingest the data that we get from the data source. Used HSQLdb for the database to store, retrieve and manipulate the data.

Used JPA and JPQL to interact with the repositories. Used React, HTML, and CSS Grid for the front end. Containerized the application using Docker.

Deployed scalable microservices on the cloud- Java, REST API Spring, AWS EKS, Docker, Kubernetes, Jenkins

Worked on Angular 8 to create a frontend JavaScript-based web page. Using REST API- SpringBoot and Java language in the backend. Containerized the application using Docker and deployed it on a Kubernetes orchestration platform Amazon Web Services EKS.

Created a git repository and a Continuous Integration and Continuous Deployment CI/CD pipeline with the help of Jenkins implementing Agile Software Development Life Cycle (SDLC) using DevOps.

Multilingual Hate Speech Detection:[Link]- Python, Pytorch, mBERT, LASER, XLMR, Adapters, Tweepy apis

Collected real-time tweets from Twitter using Tweepy, preprocessed the tweets using data science techniques.

Used mBERT and LASER embeddings on LR to train the model, which classifies hate speech over different languages (47-70% accuracy); used XLMR and adapters on mBERT for further improvement (60-82% accuracy).

Kinship Classifier:[Link]- Python, TensorFlow, Keras, VGG FaceNet, Resnet 50, Multilayer Network, NumPy

Created a model which classifies if two people are related by blood by looking at their facial images. Trained the model on more than 500000 image combinations and tested on 100000 image combinations.

Used Computer Vision methods such as VGG FaceNet (accuracy of 67.62%) and VGG FaceNet on top of Resnet50 architecture to achieve an accuracy of 94.62%.

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