QA Engineer

As a QA Engineer at Awin, your main responsibilities will include creating detailed test plans and test cases, executing comprehensive testing for AI and LLM-based applications, and identifying and tracking defects to ensure continuous product improvement. You will also work collaboratively with cross-functional teams to establish testing best practices and provide valuable feedback to enhance system usability.

The position of QA Engineer at Awin requires a minimum of 3 years of experience in software quality assurance, with at least 1 year focused on AI/ML systems. Applicants should have a solid understanding of the software testing pyramid, expertise in Python or JavaScript for automated testing, and hands-on experience with testing tools like Selenium and Postman.

At Awin, the culture is dynamic, inclusive, and socially engaging. They prioritize mental health and wellbeing through initiatives like a flexible four-day workweek and hybrid working opportunities. The company fosters diversity and encourages employees to bring their unique experiences to the team, creating a supportive environment for everyone.

QA Engineers at Awin will be involved in various testing types, including functional, regression, performance, and security testing of AI solutions. Additionally, you will conduct testing for bias, fairness, and explainability of AI outputs, ensuring that our products meet high ethical standards in AI.

Yes, Awin supports remote work for the QA Engineer role. They provide a monthly allowance to help cover the costs of remote working and support you in setting up your workspace effectively. This flexibility allows you to maintain a healthy work-life balance while performing your job duties.

When creating a test plan for an AI application, start by understanding the key functionalities of the AI system and the specific requirements of the stakeholders. Define the scope of testing, priorities, and metrics for success. Collaboration with developers and product managers will be crucial to ensure all scenarios are covered, including edge cases unique to AI outputs.

In answering this question, highlight any specific projects you've worked on involving AI or ML testing. Discuss your familiarity with testing frameworks and your approach to validating AI functionality, including how you've handled performance testing and bias detection in AI models.

To test for bias in AI outputs, I would employ various strategies such as examining the training data for underrepresented groups, using statistical methods to evaluate model predictions, and implementing fairness metrics. Collaboration with data scientists to ensure ethical standards are met is also vital in this process.

In your response, describe the situation by detailing the context of the bug, how you discovered it, and the impact it had on the system. Emphasize your problem-solving approach, how you communicated with the development team, and the steps taken to resolve the issue effectively.

Mention specific tools like Selenium or Postman, explaining your reasoning behind your preference. Discuss how these tools enhance testing efficiency, align with the project requirements, and how they have improved your testing processes in previous roles.

To stay updated, I regularly follow industry publications, participate in webinars, and engage with online QA and AI testing communities. I also prioritize professional development through courses that enhance my skills in testing methodologies and emerging AI technologies.

For performance testing of AI applications, I would start by defining performance criteria based on expected usage scenarios. Utilizing load testing tools, I would simulate real-world user load conditions and analyze system behavior, response times, and resource utilization to ensure optimal performance.

Collaboration with cross-functional teams typically begins with establishing clear communication channels. I prefer weekly meetings to align on project goals, share testing insights, and identify any potential blockers. Regular interactions foster an understanding of each role's contributions, making our collaborative efforts more effective.

Static analysis tools are utilized to analyze code for potential vulnerabilities, code quality, and adherence to standards without executing the program. I have used these tools during the early stages of development to identify issues proactively, allowing for timely resolutions before they could affect production.

Effective communication of test results involves creating clear and concise reports that summarize findings, including any discrepancies or defects. I prefer to provide visual elements, such as charts or dashboards, to highlight key metrics, ensuring stakeholders easily interpret the results and understand the next steps.