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Niche Agentic AI QA leadership requires specialized skills, reducing qualified candidate density.
Highly specialized Agentic AI QA skills limit cross-industry transferability.
Explicit 7–12 years, mandatory GenAI QA experience, tooling and leadership requirements enforce strict shortlisting.
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Define and execute testing strategies for LLM-based multi-agent and Agentic AI systems, including validation of autonomous agent behavior and AI output evaluation.
Build automated evaluation pipelines, dashboards, and CI/CD-integrated quality gates to track AI quality KPIs such as hallucination rate, agent success, and task completion rates.
Lead and mentor a team of Agentic AI Quality Engineers, set testing standards and governance models, and collaborate with cross-functional teams to improve AI quality.
7–12 years of experience in Software Testing, Quality Engineering, or Test Automation.
Minimum 3+ years of hands-on experience in GenAI, LLM Testing, Agentic AI Testing, or AI Quality Engineering.
Strong experience with automation tools such as Python, Playwright, Pytest, API automation, and CI/CD quality gates.
Experience defining AI quality metrics, evaluation methodologies, benchmarking frameworks, and working with AI evaluation tools like DeepEval, Ragas, LangSmith, or OpenAI Evals.
Experienced in testing enterprise Agentic AI platforms and autonomous AI systems with a deep understanding of LLMs, prompt validation, multi-agent orchestration, and AI safety practices.
Proven leadership skills in building and mentoring QA teams focused on AI quality engineering, capable of driving innovation in AI testing methodologies.
Skilled in collaborating with Product, Engineering, Data Science, and AI Research teams and managing stakeholder communications about AI quality assessments, risks, and KPIs.