Agentic QA Engineer – Generative AI & Multi-Agent Systems
Fiona Solutions Inc
Location: Texas
Job Type: Full Time
Salary: Competitive
Duration: Not Specified
Experience: Strategic QA, LLM, multi-agent systems
Job Description & Details
"The AI landscape is exploding with generative models and autonomous agents, creating a massive demand for rigorous quality assurance. Companies need specialists who can build resilient, scalable testing frameworks to keep these systems reliable and compliant. This role at Fiona Solutions offers a unique chance to shape QA standards for cutting\u2011edge LLM and multi\u2011agent technologies.\n\n# Job Summary\nThe Agentic QA Engineer will design and implement QA frameworks for production\u2011grade large language models and multi\u2011agent systems. Responsibilities include ensuring system resilience, scalability, and regulatory compliance across end\u2011to\u2011end AI workflows, while collaborating with engineering and product teams.\n\n# Top 3 Critical Skills Table\n| Skill | Why it's critical | Mastery Level |\n|-------|-------------------|---------------|\n| LLM QA Framework Development | Ensures reliable performance of large language models in production | Senior |\n| Multi-Agent System Testing | Validates interactions and stability across autonomous agents | Senior |\n| AI Workflow Compliance | Guarantees regulatory and ethical standards are met | Senior |\n\n# Interview Preparation\n1. **How would you design a testing framework for a production\u2011grade LLM?**\n *What the interviewer is looking for:* Understanding of model evaluation metrics, data pipelines, and automated testing strategies.\n2. **Explain methods to assess resilience and fault tolerance in multi\u2011agent systems.**\n *What the interviewer is looking for:* Knowledge of stress testing, chaos engineering, and inter\u2011agent communication validation.\n3. **What compliance considerations are unique to generative AI, and how would you verify them?**\n *What the interviewer is looking for:* Awareness of bias mitigation, data privacy, and regulatory guidelines.\n4. **Describe a scenario where scalability becomes a bottleneck in AI workflows and how you would address it.**\n *What the interviewer is looking for:* Experience with load testing, distributed architecture, and performance optimization.\n5. **How do you integrate QA processes into continuous deployment pipelines for AI models?**\n *What the interviewer is looking for:* Familiarity with CI/CD tools, model versioning, and automated rollback mechanisms.\n\n# Resume Optimization\n- QA frameworks\n- Large Language Models (LLM)\n- Multi\u2011agent systems\n- Resilience testing\n- Scalability assessment\n- AI compliance\n- Production\u2011grade AI\n- End\u2011to\u2011end AI workflows\n- Strategic testing\n- Texas\n\n# Application Strategy\nWhen reaching out to the recruiter, send a concise email greeting, attach your resume, and clearly highlight your top relevant skills. Make sure to mention related skills you possess, such as LLM QA framework development, multi\u2011agent system testing, and AI workflow compliance, and reference specific projects where you applied them.\n\n# Career Roadmap\n| Current Role | Typical Experience | Core Focus | Next Position |\n|--------------|--------------------|------------|---------------|\n| Agentic QA Engineer | 3\u20115 years in AI QA, LLM testing | QA frameworks, compliance | Senior AI QA Engineer |\n| Senior AI QA Engineer | 5\u20117 years, lead testing projects | Strategy, mentorship | AI QA Lead |\n| AI QA Lead | 7\u201110 years, cross\u2011functional leadership | Program oversight, governance | Director of AI Quality |\n"