Narasimha Karthik Jwalapuram

I am Narasimha Karthik Jwalapuram, a senior student of Electronics and communications Engineering, at PES University.

Currently, working in the field of Machine Learning and Artificial Intelligence in Python3 along with working experience in data science libraries and frameworks including Tensorflow and PyTorch.
Well versed in working with Hardware components like microcontrollers i.e Atmega320 and hardware descriptive languages like Verilog HDL.
I am an experienced Front End web developer with working experience in front end technology like HTML5, CSS3, Bootstrap, Javascript and jQuery.
I am also an active programmer with programming experience in C++, C, and Python3.
I am extremely keen on meeting and working with new people and learning new skills and technologies and also enhancing my current skills.
Thank You

Projects

Ping Knock

Come, play and win big on Cellarx!JavaScript, HTML5, CSS3

Alaram to Launch Youtube Video

Built using Python3, this alarm launches a youtube video randomly from a set of links at the time specified by the user.Python

Skills

Python

Solidity

Machine Learning

iOS

Web Designing

Experience

Team Aeolus - Member
Team Aeolus is focused on building drones for various national and international drone competitions.
Working with Robot operating system with C++ and Python
Invento Robotics - Software Developer Inter
October 2021 - Present
• Designed and developed the ’Invento Fleet’ iOS app in 5 months. Led the development of the project and is responsible for designing and building 5+ core app features independently. [SwiftUI, Swift, UIKit]
• Gained 1+ year experience in building production applications and its deployment.
• Experienced in designing the audio-video communication infrastructure between the robot and the iOS application.
Used TwilioVideo and WebRTC to develop the same. Thus reducing the dependence on Fleet Web portal by 90%.
[WebRTC, Python, NodeJS, swift]
• Developed novel AI algorithms and models to enable smooth user interaction. Developed methodology for fail-proof
Fall Detection algorithm. This methodology reduced the amount of ’True Negative’ errors in fall detection by 95%.
[Python, PyTorch, Mediapipe, ROS]
• Fixed bugs and issues through effective communication with cross-functional teams and clients.