• Rare Sense is a Web 3.0 startup that allows creators/celebrities to give their fans access to one-of-a-kind experiences in real life and inthe Metaverse.• Worked on their core product, Superfandom.io, as a full-stack developer. Used Next.js for both, front-end and back-end, CSS forstyles, Redux for state management, and MongoDB for database management.• Planned, developed, and maintained multiple full-stack features that allowed admins to manage orders and NFTs.• Wrote test scripts in Next.js that were used to test server load based on the number of concurrent users. JMeter was used to maintainconcurrency• Created detailed plans and timelines that work towards the fulfillment of set product objectives.

• Part of team Waafi which is responsible for building a chat app that is integrated with their main product: a money transfer service app.• Built the web version of the chat app and worked with the iOS and Android developers to integrate it with the mobile versions of the app.• Used React, CSS, and Material-UI for the front end and React Hooks for state management.• Implemented sockets using the Socket.IO library for bi-directional communication between the clients and the servers.• Handled API requests and sockets in the backend using Node.js and Express.js

• Developed a dashboard from the ground up that visualizes individual car wash data to make it easier for car wash owners to track how their business is running.• Used React and CSS for the front end and Redux for state management.• For the back end, used MongoDB, Mongoose, Node.js and Express.js• Deployment was handled by AWS EC2 on an NGINX server.• Converted UI and UX designs to front-end code to create modular components for use throughout the codebase.

• Enhanced user functionality and user experience on web pages by developing a UI using JavaScript and Vue• Converted different component designs to front-end code using Vue.js and Vuetify• Implemented multilanguage support to the entire client facing website using Internationalization (i18n)

• Developed and deployed an app called “SmartEats” to the Apple App Store that finds restaurants in the user’s vicinity that meet the user’s budget• Tracked user and restaurants’ locations on the map using Google Maps API• Used EatStreet’s API for restaurants’ data and Alamofire for API handling; worked with JSON objects

• Analyzed information in current database to assess and predict customer types• Created DataFrames using Pandas and visualizations for data interpretation using Bokeh• Python scripts calculate customers’ age, print cities’ list to check for spelling variations, remove invalid dates, and split data of different customers into separate files