Akhil Chandail

Software Engineer @ Cavista | 3.5+ Years of Experience | akhilchandail9@gmail.com | My Projects: https://rb.gy/cs0ahh

india

Book 1:1

JavaScript

TypeScript

VueJS

ReactJS

MySQL

redux

NodeJS

NextJS

Python

Django

Experience

3 years, 8 months

Axxess

Software Engineering I

Jun 2024 - Present (3 m) • pune, india • full-time

Associate Software Engineer

Jul 2022 - Present (2 yr, 2 m) • pune, india • full-time

Feature Development for Axxess Home Health Solution: Developed user interfaces for 25+ clinical and operational features with seamless backend API integration, including reusable Vuex-based UI components.
Dynamic Form Builder Components: Led the Vuex-based development of dynamic form builder components, enhancing user input accuracy, seamless data integration, and overall user experience.
Technical Debt and Bug Resolution: Spearheaded the resolution of 10+ technical debt tickets and 50+ bugs. Utilized advanced debugging tools and techniques to identify and fix issues, leading to a significant 30% improvement in software performance and stability.
Migration to Vue Components: Converted ASPX and Razor pages into Vue.js components, modernizing the codebase and improving maintainability. This migration facilitated better component-based architecture, allowing for more modular and scalable development.

JavaScript

ReactJS

TypeScript

VueJS

SASS

jQueryCSS3

docker

HTML

Git

Cavista

Software Engineering Intern

Mar 2022 - Jun 2022 (3 m) • remote • full-time

Self Employed

Freelance Web Developer

Dec 2020 - Feb 2022 (1 yr, 2 m) • remote • full-time

Conceptualized and developed web applications for clients, managing UI-UX design, content creation and end-to-end development, contributing to their successful launch and functionality.

Some of the clients:

Web Development

FirebaseDjango REST FrameworkPython (Programming Language)

Wordpress

Figma

JavaScript

leadingindia.ai

Deep Learning Research Intern

Jun 2019 - Jul 2019 (1 m) • noida, india • full-time

Conducted groundbreaking research in deep learning, specializing in training ML models for object detection and survival prediction in the Titanic disaster.
Utilized advanced technologies, including DGX-NVidia GPU, Google’s Open Source Images Dataset V5, and Computer Vision.