Jenil

Hello 👋, I'm

Jenil Vekaria


Software Developer

Who builds and designs exceptional high-quality applications.

My Intro

About Me

Profile

Hi 👋, I'm Jenil! I graduated from Ryerson University in 2022 with a Bachelors degree in Computer Science. I'm a software developer specialized in developing and designing exceptional high quality web/mobile applications that run seamlessly across all platforms & devices


Computer Science has vast subdisciplines and as technologies advance quickly, I always enjoy trying out new technologies to keep things interesting and challenging. My passion for programming started back in grade 8 creating my first mobile game and ever since then I began to appreciate the smart and efficient methologies that goes behind all the technologies I've ever used. I'm always learning new technologies through school, hackathons and personal projects.


Aside from being in front of my computer, I enjoy outdoor activities like playing Basketball 🏀, Biking 🚴‍♂️, Swimming 🏊‍♂️(former Lifeguard), and Skating ⛸.


Education


Ryerson University

BSc in Computer Science (Co-op)
September 2017 - May 2022

CGPA: 3.85 / 4.33


West Humber Collegiate Institute

High School Diploma
September 2013 - June 2017

Average: 88 / 100

Skills


Languages
Frontend / Backend
Tools
Databases
DevOps

Certifications

 AWS Educate Getting Started with StorageAWS Educate Introduction to Cloud 101
My experience

Work Experience

Software Developer Co-op

Ontario Ministry of Health and Long-Term Care
January 2021 - August 2021
  • Worked on the development of Case and Contact Management project, leveraging Salesforce technology such as Apex, SOQL, Visualforce, and JavaScript (similar to Java, SQL, and HTML), to improve pandemic response for health workers across Ontario
  • Improved project workflow through managing Copado CI/CD pipeline and synchronizing environments, reducing build failure
  • Defined coding requirements, implemented user stories, and prepared technical documentation during each sprint
  • Coordinated with the QA team to efficiently identify and resolve bugs, enhancing the project’s overall quality and functionality
  • Presented feature implementations to stakeholders and actively sought feedback for continuous project improvement efforts

Software Developer Co-op

ServiceEcho
May 2020 - August 2020
  • Spearheaded the development of ServiceEcho’s Android app using Java, SQL, MVP architecture, and design patterns (Factory Method, Builder, Singleton) to build robust and scalable application that serves 500+ users across 19 industries in North America
  • Migrated the legacy library to AndroidX library that reduced the app size by 20%, eliminating code redundancies, provided codebase modularity and consistency, improved app performance, and backward compatibility for older Android users
  • Improved application performance through integrating Google Crashlytics API, eliminating 99% of production crashes
  • Collaborated closely with the IOS developer to maintain consistency in the Android app to achieve a cohesive user experience
Check it out!

My Projects

Filter

Quill

Quill is an intelligent note taking app with AI integration built using OpenAI API, vector embedding, and Pinecone.

NextJs

OpenAI

Pinecone

Tailwind CSS

TrackIt - Project Management System⭐

Issue and Project Tracking System that allows team members to collaborate, discuss and manage projects effectively.

React

NextJs

NodeJs

ExpressJs

MongoDB

Groundbnb

Groundbnb is an Airbnb clone that support functionalities such as listing and reservation creation, view trips/reservation/properties, favoriting and advanced filtering

NextJs

TypeScript

React

MongoDB

Entertainment Movie

Entertainment Movie is a website where you can browse all the popular, latest, and upcoming movies. By signing in, you can favourite movies and add it to watchlist, so you will never miss out!

React

Redux

MongoDB

NodeJs

ExpressJs

Momentum Clone

Personal Dashboard with Digital Clock, Welcome Message, Daily Weather and Inspirational Quote of the Day

React

Waste Image Classification

Developed Convolution Neural Network using Keras to classify images of trash, glass, cardboard, plastic, and metal. Achieved an accuracy of 80%.

Python

Keras

Machine Learning

Reach out!

Contact Me

Got feedback on my

portfolio or invitation to coffee ☕, my inbox is

always open.