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Recognizable enterprise brand, metro location, and mid-level generalist title increase competition.
Core QA skills are transferable, but GenAI-focused testing increases domain-specific bias.
Explicit 3+ years, GenAI QA expertise, and specific tooling requirements make shortlisting relatively strict.
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Develop and implement comprehensive testing strategies and automated test suites specifically for GenAI features including conversational AI and LLM workflows.
Create evaluation frameworks and monitoring systems to measure and maintain GenAI quality across dimensions such as accuracy, relevance, safety, consistency, and latency.
Perform adversarial testing to identify failures, biases, or security vulnerabilities and collaborate with engineering teams to set acceptance criteria and quality gates for AI feature releases.
3+ years of QA or test engineering experience, preferably with AI/ML systems.
Strong understanding of GenAI technologies including LLMs and prompt engineering.
Experience with test automation frameworks and scripting (Python, JavaScript) and knowledge of software testing methodologies including functional, integration, regression, performance, and security testing.
Experience with API testing (Postman, REST Assured) and familiarity with CI/CD pipelines and automated testing integration.
Experienced in testing GenAI or AI systems with knowledge of LLM evaluation frameworks and AI-specific quality metrics.
Comfortable designing tests for non-deterministic systems and working closely with cross-functional engineering, data science, and product teams.
Capable of building tools and frameworks to facilitate GenAI testing and monitoring, with a good understanding of responsible AI principles including fairness, transparency, and safety.