ALM – Gen AI Quality Engineering Required Skills & Qualifications 8–12 years of experience in Quality Engineering 3+ years of hands-on experience in testing GenAI applications/ Strong expertise in API testing, automation frameworks and release governance Deep understanding of: RAG Prompt engineering LLM evaluation techniques (Evals) Agent workflows Strong knowledge of API security testing and OAuth-based systems Experience integrating testing within CI/CD pipelines Cloud platform familiarity (AWS) Excellent communication and stakeholder management skills Role Summary We are seeking a Senior Lead Quality Engineer (Gen AI) to define and drive the end-to-end Quality Engineering strategy for AI-powered systems, including LLM-based applications, AI assistants, Retrieval-Augmented Generation (RAG) pipelines, and agent-driven workflows. This role will establish measurable AI quality standards, governance frameworks, automation strategy, and release readiness criteria. The ideal candidate brings strong QE leadership along with hands-on expertise in LLM behavior, contextualization techniques (Prompt Engineering, RAG, Fine-Tuning), evaluation frameworks (Evals), accessibility, API security, and AI observability. You will work cross-functionally with Product, Engineering, and Platform teams to ensure AI systems are accurate, safe, reliable, scalable, and production-ready. Key Responsibilities AI Quality Strategy Define and own the AI Quality Engineering roadmap and standards Establish measurable success criteria for Gen AI features Design quality gates and release readiness frameworks Lead AI risk assessment and regression strategy AI Feature & Evaluation Understanding Define and track AI-specific KPIs, including: Intent accuracy Context retention (multi-turn reliability) Hallucination rate Bias & toxicity scores Guardrail breach frequency Fallback & recovery rate Retrieval precision/recall (RAG) Token efficiency P95/P99 latency Additional responsibilities: Design benchmark datasets for deterministic AI regression Implement automated and human-in-the-loop evaluation frameworks Build structured AI scoring rubrics and quality dashboards Contextualization & LLM Validation Design and validate prompt engineering strategies Validate RAG (chunking, embeddings, retrieval relevance) Evaluate fine-tuned models and dataset integrity Validate agent-based and multi-step reasoning workflows Safety, Security & Responsible AI Ensure PII protection and data boundary enforcement Conduct API security validation, including: Authentication & OAuth flows Role-based access control Cross-account/user isolation Rate limiting and throttling Malformed input and abuse scenarios Ensure compliance with enterprise AI governance standards Accessibility Validate compliance with WCAG standards Ensure screen reader and keyboard navigation compatibility Performance, Reliability & Observability Define and validate latency benchmarks and SLAs Conduct scalability and concurrency testing Monitor token usage and cost efficiency Validate feedback, fallback mechanisms and recovery behavior Automation & CI/CD Integration Build AI-focused automated regression frameworks Integrate AI validation into CI/CD pipelines Enable continuous AI quality monitoring across releases Ensure backward compatibility across API and platform versions About Adobe Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. 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