QA Engineer

• Define GenAI-Specific QA Strategy
• Develop a QA framework tailored to GenAI systems and workflows.
• Design tests for: Prompt behavior across varied inputs and user tasks; Hallucination detection; Factual consistency and groundedness.
• Blend manual and automated test design for both deterministic and stochastic outputs.
• Collaborate with teams to obtain or create sample data with clear target outputs.
• Own end-to-end test strategy and execution for GenAI-powered features.
• Ensure coverage across: Diverse prompt phrasing, user intents, and failure modes; Multiple GenAI features (e.g., summarization, generation, classification); High-risk, edge-case, and compliance-driven scenarios.
• Lead the design and implementation of prompt and model evaluation protocols: Alignment between user input and intended behavior; Output fluency, tone, and coherence; Clarity, coverage, and relevance of responses.
• Use Golden datasets and benchmark prompts to establish evaluation baselines.
• Design and manage SME-driven review workflows.
• Facilitate structured reviews focused on: Correctness/accuracy based on metrics and SME feedback; Capturing edge-case failures.
• Define and track QA effectiveness using metrics such as: Pass rate for high-risk use-cases; HITL reviewer agreement rates and flagging critical issues; Use-case specific measures of “quality”.
• Deliver clear, actionable dashboards and reports to leadership on AI quality, safety, and readiness.

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