Abhiram Jaligama

Summary

Adaptable and eager learner with academic and technical foundation seeks opportunities to contribute and grow. Collaborative developer with full software development lifecycle understanding, valuing teamwork and continuous learning to support team goals effectively.

Work History

Android Developer - Intern

Verzeo Edu tech

01.2023 - 02.2023

Developed and implemented Android applications, gaining valuable hands-on experience in the full software development lifecycle.
Provided ongoing support to senior developers by assisting with design, development, testing, and maintenance of new programs using Dart. Contributed to weekly sprints.
Actively participated in workshops to stay current with the latest Android development techniques, demonstrating a commitment to continuous learning.
Collaborated effectively with a team to design and build an e-commerce app using Java and Dart, enhancing my real-time problem-solving skills through teamwork.
Awarded Internship Certificate for outstanding performance and contribution.

Education

B.Tech - Computer Science

Lovely Professional University

Phagwara, IN

Higher Secondary - MPC

Narayana Junior College

Hyderabad, IN

03-2020

Skills

Programming Languages

Java
Python
Java Script

Web Technologies

HTML

CSS
Bootstrap

Database Management

MySQL

Platforms

Ms Windows98, 2000, XP, Vista, Windows7, Window 10 ,11

Soft Skills

Adaptability
Team Player
Creativity
Presentation skills

Affiliations

Participated in coding competition conducted by Coding Ninjas in July 2023
Led a team of volunteers in planning and executing successful event in May 2023

Projects

Library Management System - Java

Developed user-friendly library management application using Java, Spring and SQL.
Simplified and automated various library operations like book management, patron management, and administrative tasks.
Implemented a digital solution for efficient management of books, patrons, and administrative tasks.

Gym Pose Estimation - Python

Developed a Python-based gym pose estimation model which is used to analyze gym exercises and provide real-time feedback for improved form and technique.
Prepared data pipelines for data validation, data transformation & data pre-
processing.
Used supervised learning technique like KNN to estimate the Gym pose. Achieved 96.7% of accuracy using this model.

Hobbies

Photography

Timeline