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Metro location and mid-level requirement, but niche GenAI QA specialization moderates applicant density.
GenAI-focused QA skills are specialized and less transferable outside AI product contexts.
Mandatory 3+ years QA experience, GenAI expertise, and specific automation/tooling requirements make filters stringent.
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Develop and implement comprehensive testing strategies and automation frameworks specifically for GenAI features including conversational AI and LLM-powered workflows.
Build and maintain evaluation frameworks and quality metrics to measure GenAI accuracy, relevance, safety, consistency, and latency across diverse user scenarios.
Collaborate with engineering and product teams to define acceptance criteria, conduct adversarial and user acceptance testing, and monitor production quality of AI systems.
3+ years of QA or test engineering experience, preferably involving AI/ML systems.
Strong understanding of GenAI technologies including LLMs and prompt engineering.
Experience with test automation frameworks and scripting (Python, JavaScript), UI testing tools (Playwright, Puppeteer), and API/backend testing tools (Postman, REST Assured).
Knowledge of software testing methodologies (functional, integration, regression, performance, security testing) applicable to non-deterministic AI systems.
Experienced in designing and executing QA strategies tailored to AI-driven and non-deterministic applications at scale, especially involving GenAI and LLM technologies.
Proficient in implementing automated testing and monitoring in continuous integration/deployment pipelines for AI features, with a strong analytical and problem-solving approach.
Comfortable collaborating cross-functionally with engineers, data scientists, and product teams to influence AI product quality standards and lifecycle.