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QA Engineer – AI Systems

Posted an hour ago
Worldwide
0-2 years experience
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AI Summary

Evaluate AI agents and multi-step workflows for accuracy, consistency, and reliability across web and API layers. Document defects, troubleshoot root causes, and collaborate with engineering teams to ensure high-quality test coverage.

This is a remote position.

We are looking for a QA Engineer to help us maintain the quality and reliability of our AI-powered products, including AI Assistants and agent AI systems.


Key Responsibilities

  • Evaluate AI agents for accuracy, consistency, and appropriate handling of edge cases and failures

  • Assess multi-step, multi-tool AI workflows to confirm agents behave as intended across an entire task, not just a single response

  • Conduct functional, regression, integration, and user acceptance testing across web and API layers

  • Identify, document, troubleshoot, finding root cause and track defects, working closely with the development team through to resolution

  • Maintain clear and thorough test documentation for both conventional features and AI-driven functionality

  • Support automation testing initiatives where relevant, or help establish them if they do not yet exist

  • Work with product and engineering teams to translate requirements into meaningful test coverage



Requirements

  • 1–3 years of professional experience in quality assurance or software testing

  • Experience with automation testing tools such as Selenium, Playwright, or Cypress

  • Experience testing AI chatbots, AI Assistants, or agent AI systems

  • Solid grounding in QA fundamentals, including test case design and defect lifecycle management

  • Ability to test systems where outcomes are not always fixed or fully predictable

  • Strong written communication skills, particularly when explaining AI behavior to non-technical stakeholders

  • Ability to work independently and reliably in a remote setting

  • Familiarity with API testing tools such as Postman

  • Exposure to prompt evaluation or LLM output assessment methods



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