QA Engineer (AI/ML Automated Product Build)

Updated Job Description: QA Integration Engineer (AI/ML Automated Testing Focus) We are seeking a QA Integration Engineer to join our team and design an AI/ML-based automated testing tool that integrates into our current product suite. The role involves setting up a cutting-edge testing framework for our Dart server-side framework while leveraging AI/ML to enhance efficiency and accuracy. This is a pivotal position that requires technical expertise, creativity, and a willingness to innovate. Key Updates to the Role • Design and implement AI/ML-based testing automation. • Build upon traditional unit testing frameworks to incorporate AI-driven insights and predictive analytics for test case generation and issue diagnosis. • Ensure the tool’s reliability through regular updates and align it with project advancements. Key Responsibilities: • AI/ML Testing Framework Design: • Develop and deploy an AI-driven automated testing framework for our Dart backend using tools like dart test, Mockito, and ML models. • Integrate AI to dynamically generate, optimize, and prioritize test cases. • Automated Testing Integration: • Implement real-time feedback loops using AI/ML to identify flaky tests and optimize test execution. • Set up CI/CD pipelines to incorporate automated testing results. • AI-Driven Insights: • Utilize ML algorithms to analyze code changes and predict potential areas of failure. • Provide actionable reports based on AI-generated data to guide developers. • Documentation & Maintenance: • Document the AI/ML testing framework for scalability and ease of use. • Perform monthly updates to maintain alignment with the evolving codebase and emerging AI technologies. Qualifications: Required: • Proven experience in setting up unit testing frameworks for backend systems. • Proficiency in Dart programming and testing libraries (e.g., dart test, Mockito). • Demonstrable experience with AI/ML tools and libraries (e.g., TensorFlow, PyTorch, scikit-learn) and applying them to software testing. • Familiarity with CI/CD tools (e.g., GitHub Actions, GitLab CI, Jenkins). • Strong organizational skills and experience in documenting technical frameworks. Preferred: • Experience integrating static code analysis tools like SonarQube. • Knowledge of real-time monitoring tools for backend systems. • Familiarity with AI tools like DeepCode, Codacy, or CodeWhisperer. Deliverables: • AI/ML-Powered Testing Framework: • Modular framework capable of test case generation, optimization, and reporting. • Automated prediction and analysis of potential failures based on code changes. • Mocking and stubbing capabilities for Dart backend services. • Monthly Maintenance and Updates: • AI model retraining to adapt to evolving codebases. • Continuous refinement of test cases based on real-time insights. • Documentation: • Comprehensive guides for onboarding team members and ensuring framework extensibility. • Integration with Product Suite: • AI/ML testing tool seamlessly integrated with the current CI/CD pipeline. • Actionable coverage and performance reports for developers. Screening and Evaluation Questions: • AI/ML Knowledge: • Can you describe how you would train an ML model to identify potential areas of code failure based on historical test data? • What AI/ML libraries or tools have you worked with, and how did you apply them to testing or development? • Dart-Specific Knowledge: • How would you incorporate AI-driven testing alongside traditional Dart tools like dart test or Mockito? • How would you ensure the AI/ML testing framework aligns with Dart’s asynchronous architecture? • Integration and Automation: • Have you set up a CI/CD pipeline for automated testing with AI integration? If so, describe your process and the challenges faced. • How would you manage and address flaky tests in an AI-driven framework? • Tooling and Innovation: • What AI/ML-driven testing tools have you used or researched (e.g., DeepCode, Codacy, or others)? • Can you provide an example of how AI or ML improved a testing framework you worked on? • Documentation & Scalability: • How do you document complex AI/ML frameworks to make them accessible to non-technical team members? • How would you ensure the testing tool evolves with the codebase? • Collaboration and Adaptability: • How do you work with developers to ensure the AI/ML testing framework is widely adopted and maintained? • Have you ever adjusted an AI-based solution to fit the team’s workflow? Describe the situation and outcome. What We Offer: • Flexible work schedule. • Competitive pay, including a MVP fee, MVP milestone bonus, and equity for ongoing updates and improvements. • The equity offering is designed to reward long-term commitment and contributions, giving you a stake in the success of our innovative product suite. • Opportunity to innovate with a team using cutting-edge AI/ML and server-side technologies. Project Milestones: • Initial Delivery: Fully operational AI/ML testing framework within 45 days. • MVP Milestone Bonus: Upon successful delivery of the MVP framework, a one-time bonus will be awarded to recognize your contribution. • Ongoing Equity Compensation: Monthly updates, improvements, and AI model retraining to maintain quality and adaptability will be rewarded with equity to align incentives with the company’s growth. This role is ideal for innovators passionate about merging AI/ML technologies with software testing to redefine quality assurance. If this sounds like you, we’d love to hear about your experiences, ideas, and vision for testing automation! Apply tot his job