QA Engineer – AI Systems

Earnix is a leading provider of advanced pricing and rating solutions tailored for the insurance and banking industry. Our software empowers actuaries and data scientists within financial companies to make data-driven decisions and optimize pricing strategies. With a commitment to innovation and a deep understanding of the market, Earnix is at the forefront of revolutionizing how financial companies approach pricing and rating.

We are looking for a talented QA Engineer to ensure the quality and reliability of both traditional software applications and AI/LLM-powered features. The ideal candidate will combine classical software testing methodologies with emerging best practices for testing AI systems.

As a QA Engineer, you will play a key role in testing AI-powered systems and applications to ensure they meet the highest standards of quality and performance.

What you’ll do:

• Design, execute, and maintain test plans for web applications and AI-powered features.
• Create and maintain automated test suites using Python and JavaScript.
• Develop test cases for LLM-based systems, including prompt testing and evaluation.
• Document bugs, issues, and test results clearly and comprehensively.
• Validate AI model outputs for accuracy, consistency, and safety.
• Collaborate with developers and AI engineers to improve system quality.

You’ll do It using:

• 3+ years of proven experience in software QA
• Strong knowledge of Python and JavaScript.
• Experience with test automation frameworks (e.g Pytest, Jest)
• Basic knowledge of prompt engineering and LLM evaluation.
• Hands-on experience with bug tracking systems (e.g. Jira).
• Strong understanding of API testing, REST principles, and software development lifecycles.
• Analytical and problem-solving abilities.

You’ll excel by:

• Bringing experience in testing AI/ML systems and familiarity with LLM evaluation metrics.
• Demonstrating knowledge of version control systems (Git).
• Applying a basic understanding of machine learning concepts in quality assurance.
• Collaborating with cross-functional teams and contributing to high-quality AI software.
• Having a strong focus on detail and a proactive approach to problem-solving.