Asif
Mohaimen

Prime Optimus is a custom-built Python client designed specifically for Star Trek™ Fleet Command. It provides robust orchestration for various in-game scenarios, making it the ultimate solution for large-scale load testing.

Features:

• Scenario Orchestration: Seamlessly manages complex in-game scenarios for load testing.
• Powered by Locust: Integrated with a custom Locust framework to handle high-scale performance tests effectively.
• GitLab CI/CD Integration: Automatically deploys load test infrastructure using Infrastructure as Code (IaC) with Terraform.
• Smart Scheduling: Supports automated scheduling and test runs, integrated with Google Forms for convenience.
• Real-time Reporting: Get instant updates and insights with reporting integrations to Slack and Datadog.
• Fully Automated Workflow: From deployment to execution, everything runs smoothly without manual intervention.

Whether you're orchestrating tests, analyzing performance, or scaling infrastructure, Prime Optimus has you covered. It's the one-stop solution for all your load testing needs in Star Trek™ Fleet Command. 🚀

Worked for the DApp named Startrail, primarily focusing on building a Regression Suite in Python for the Startrail API. This API serves as the backbone of all transactions across the Polygon chain within Startrail.

Startbahn is leading the way in developing blockchain infrastructure for the art industry with Startrail. The platform supports the transfer of value by ensuring the authenticity of works.

Key Responsibilities:

• Test Design
• Regression Suite Building for the Startrail API
• Release Management for the backend components of Startrail and Startrail API

• Periodic Backend Tests: Ensured reliable performance of the RTB system through automated, periodic backend tests.
• End-to-End Bidding Flow: Emulated the entire bidding flow, including rendering within a native environment.
• Bidder and Win Notices Coverage:
  • Developed integration tests to cover the Bidder and Win Notices projects.
  • These components communicate with 40+ ad exchanges and supply sources to serve ads to billions of profiled internet users.

• Periodic Selenium Tests: Automated tests for the DSP Dashboard executed in a headless environment inside a Docker container.
• Jenkins Integration: Tests are triggered periodically via Jenkins pipelines.
• Feature Coverage: Ensures test coverage for most of the features available in the DSP Dashboard.

It's a smart assistant for kids. Please visit the product website for more details about the project. I worked as a Software Engineer QA for this project. My role for the project was following:
• Developed multiple test oriented tools in Java EE.
• Developed a service test framework for KinderMate in Java SE.
• Created automated tests for some core services in Java and Python.
• Worked on the backend architecture of a Conversation Engine.
• Worked on content processor engine for Safe Search for kids.
• Worked on testing full payment flow in App Store and Playstore for the app.
• Website optimization & Browser Extension tests.

• Website

In this project the goal was to determine vehicle models in real time CCTV feeds. My role and contribution for this project was following:
• Developed data collection scripts for collecting training data from various sources (e.g. Google Images, Flickr, Different IP Cam Feeds)
• Written automatic training, validation and test data processing script.
• Trained a model for detecting car models in Dhaka City Streets with very high accuracy.