Anaplan

Overview

We're pioneering a new role focused exclusively on quality assurance for GenAI/Agentic systems. As our QA Automation Engineer within AI, you'll develop testing strategies, evaluation frameworks, and quality metrics specifically designed for LLM-powered applications.

About Anaplan

At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market.

Requirements & Eligibility

• Solid QA or test engineering experience, with exposure to or a keen interest in AI/ML systems.
• Hands-on Python coding experience.
• Good understanding of GenAI technologies including LLMs, prompt engineering, and AI application patterns.
• Solid experience with test automation frameworks and scripting (Python, Pytest, API, and UI Testing).
• Practical knowledge of software testing methodologies (functional, integration, regression, performance, security testing).
• Ability to execute test cases and evaluation criteria for non-deterministic systems.
• Experience with API testing tools (Postman, REST Assured) and backend testing.
• Familiarity with CI/CD pipelines and automated testing integration.

Key Responsibilities

• Develop and execute end-to-end automated test coverage across evaluations, APIs, UIs, and performance tests using Python.
• Implement testing strategies for GenAI features, including conversational AI, agentic systems, and LLM-powered workflows within your workstream.
• Run and maintain automated test suites for prompt testing, including regression tests designed to detect unintended changes in model behaviour.
• Support and maintain evaluation frameworks to measure GenAI performance across accuracy, relevance, safety, consistency, and latency.
• Contribute to curating high-quality test datasets and "golden examples" that represent diverse user scenarios and edge cases.
• Assist in adversarial testing to identify potential model hallucinations or biases, and support the integration of production alerting systems.
• Utilise internal tools and testing frameworks to help developers easily validate their GenAI code, and assist with user acceptance testing.
• Take accountability for your workstream's automated coverage, documenting testing procedures and known issues in clear, accessible formats.