Lead design and execution of test strategies for generative AI systems (LLMs, RAG, agents). Validate accuracy, factuality, fairness, bias, consistency, and security of AI outputs. Create API and model-endpoint tests, automate test suites, perform adversarial and exploratory testing, detect hallucinations, validate data quality and performance metrics, and collaborate with cross-functional Agile/DevOps teams to ensure safe, reliable GenAI deployments.
JD Required Skills • 8+ years of experience in QA, testing, or quality engineering, with hands on exposure to AI/ML or Generative AI systems • Experience testing with chatbots, NLP based applications, and GenAI solutions, including prompt engineering and optimization • Strong understanding of AI evaluation techniques, including hallucination detection, factual accuracy, bias, and output consistency • Knowledge of Responsible AI principles, including fairness, transparency, and explainability • Experience validating data quality with a basic understanding of statistics and AI performance metrics • Proficient in API testing (REST, JSON) and testing AI model endpoints • Hands on experience with test automation tools and scripting (Python preferred) • Familiarity with the ML lifecycle, model versioning, and regression testing for AI systems • Exposure to cloud-based AI platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI • Strong foundation in software testing methodologies, including exploratory, negative, and adversarial testing • Ability to design test cases for non-deterministic AI systems • Strong analytical and critical-thinking skills, with the ability to objectively assess subjective AI outputs • Excellent documentation, communication, and collaboration skills, with experience working in Agile / DevOps, cross functional AI teams AI Tester Ensure the quality, safety, reliability, and performance of Generative AI systems by developing and executing testing strategies that validate accuracy, fairness, security, and alignment with business and ethical standards. Key Responsibilities • Design and execute test strategies for GenAI systems (LLMs, RAG pipelines, AI agents, copilots) • Validate accuracy, relevance, consistency, and factuality of AI-generated outputs
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What you need to know about the Austin Tech Scene
Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.
Key Facts About Austin Tech
- Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
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- Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
- Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
- Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center



