Harsimran S.

Harsimran S.

Senior Software Developer

IL , United States

Experience: 8 Years

Harsimran

IL , United States

Senior Software Developer

134400 USD / Year

  • Immediate: Available

8 Years

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About Me

Over 8+ years of experience in Mobile and Web application development with Android/ Java. Hands-on experience in Android application development following Androids UI Guidelines, best practices, and coding standards. In-depth understanding of Android...

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Portfolio Projects

Description

  • Implemented MVVM design pattern and followed android material design principles.
  • I had to encrypt and decrypt all texts and images. For the algorithm of crypto, AES256-GCM was used. Also in order to solve the performance issue arising in decryption processing, I handled multiple threads properly.
  • I have implemented the real-time chat services by using Push notification with Firebase cloud messaging.
  • Coordinated with BA, development team, technical lead, QA, and project manager during entire cycle using Bitbucket and Jira
  • Used Fastlane for continuous delivery.

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Description

  • Architect the project, implemented an atomic design pattern.
  • I have worked on onboarding/authentication/experience/profile flows. Also, from the project's essential, I had to solve the problems arising in implementing the geo-features, including tracking location in real-time, geo-fencing, map-clustering, and heat map. Also, I have solved the UI problems relating to various sizes of devices.
  • Handled the complex app navigation with react-navigation.

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Description

  • Analyzing the requirements, project planning, designing the architecture.
  • Used MERN stack for web solution.
  • Implemented generating the verification hash code using Node JS and added user validations and user permissions by using Node.js.
  • Created ReactJS for reusable components with material UI, background file uploads.
  • By using Expo SDK, I developed the iOS and Android apps.

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Description

  • Experienced in using xibs, storyboards, and writing custom UI components
  • Implemented MVC design pattern and followed confidential user interface guidelines
  • Setup the AWS serverless backend services(Cognito, RDS, Lambda functions, API gateway)
  • Used Onesignal with deep linking

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Description

Android App to show jokes using java and android libraries and using google cloudendpoints. Project contains a Java library for supplying jokes. Project contains an Android library with an activity that displays jokes passed to it as intent extras. Project contains a Google Cloud Endpoints module that supplies jokes from the Java library. Project loads jokes from GCE module via an AsyncTask. Project contains connected tests to verify that the AsyncTask is indeed loading jokes. Project contains paid/free flavors. The paid flavor has no ads and no unnecessary dependencies. Ads are shown in the free version. App retrieves jokes from Google Cloud Endpoints module and displays them via an Activity from the Android Library. Note that the GCE module need only be deployed locally.

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Description

Android App to show stock quotes of selected companies. Each stock quote on the main screen is clickable and leads to a new screen which graphs the stocks value over time. Stock Hawk does not crash when a user searches for a non-existent stock. Stock Hawk Stocks can be displayed in a collection widget. Stock Hawk app has content descriptions for all buttons. Stock Hawk app supports layout mirroring using both the RTL attribute and the start/end tags.

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Description

Android App to show new released and highly rated movies. Movies are displayed in the main layout via a grid of their corresponding movie poster thumbnails. UI contains an element ( settings menu) to toggle the sort order of the movies by: most popular, highest rated Movie details layout contains title, release date, movie poster, vote average, and plot synopsis. When a user changes the sort criteria (most popular and highest rated) the main view gets updated correctly. In the movies detail screen, a user can tap a button to mark it as a Favorite. The titles and ids of the users favorite movies are stored in a Content Provider backed by a SQLite database. This Content Provider is updated whenever the user favorites or unfavorites a movie. When the favorites setting option is selected, the main view displays the entire favorites collection based on movie ids stored in the Content Provider.

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Description

IOS App to record user voice and then play it out in other characters voice. Implemented multiple View Controllers for different functionality in app. Implemented Navigation Controller and Segue to move from one screen to another. Used Stack Views to properly align various UI elements. Record the user voice in audio format and pass between different views for core functionality of app.

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Description

Refactored the app to run as a container on local file system. The app was containerized using Docker file in repo using which docker image can be built. Docker images were made publicly available using dockerhub. The application runs on a kubernetes cluster in the cloud. The app can be upgraded via rolling-update without downtime using kubernetes services.

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Description

Developed serverless app on aws cloud. Data was stored on aws dynamodb. LocalSecondary indexes & Global Secondary indexes used for data fetching from dynamodb. Event driven approach was adopted to send notifications using web sockets. Serverless framework was used to develop and deploy app to aws cloud. Postman collection made available on github.

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Description

Book app which have main page showing 3 shelves for books, Reading, WantToRead, Read. Each book is shown on the correct shelf, along with its title and all of its authors. The main page shows a control that allows users to move books between shelves. Search capability is provided to user to search for books. As the user types into the search field, books that match the query are displayed on the page, along with their titles and authors The search page contains a link to the main page. When the link is clicked, the main page is displayed and the URL in the browsers address bar is /.

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Description

Created working model for predicting house prices using Boston housing dataset. All requested statistics like mean, median, standard deviation for the Boston Housing dataset are accurately calculated using numpy. Identified whether the hypothetical model successfully captures the variation of the target variable based on the models R^2 score. Training and testing split is correctly implemented in code. Identified the trend of both the training and testing curves from the graph as more training points are added. Used K-Cross validation technique in grid search technique to optimize the mode.

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