AI QA Engineer
AI-driven SaaS products introduce quality problems that conventional testing was not designed to catch. Model-backed workflows produce non-deterministic outputs. Recommendation logic drifts. AI-generated content must be evaluated for correctness and consistency, not just rendering. Functional testing remains essential — and it now shares the stage with a different class of verification challenge.
This role covers both. You design test strategies, execute manual and automated testing across functional, regression, integration, and exploratory dimensions, and increasingly turn your attention to the behaviour of AI/ML outputs embedded in product workflows. You hold end-to-end quality — from test plan design through defect resolution — and work directly with engineering, product, and design to surface quality risks early.
Who thrives here
You have a strong manual testing foundation and have built comfort with automation tooling. You think in edge cases. You are beginning to ask what "correct" means when the system under test is a model, not a deterministic function. You are already using AI tools in your day-to-day work and want to build expertise in a discipline — AI output validation — that most QA practices are only starting to formalise.
What you'll own
- Design and execute test plans, test cases, and test strategies for AI-driven SaaS products with comprehensive coverage.
- Perform functional, regression, integration, and exploratory testing across product workflows.
- Document and track defects through to resolution, maintaining clear records.
- Develop and maintain automated test scripts, including with tools such as Selenium.
- Balance manual and automated approaches to match the needs of each project.
- Partner with engineering, product, and design to anticipate quality risks before they reach production.
- Analyse test results to identify bottlenecks and drive fixes with developers.
- Contribute to process-improvement initiatives within the QA practice.
- Validate AI/ML outputs — model-backed workflows, AI-generated content, recommendation logic — for correctness, consistency, and edge-case behaviour.
- Use AI tools such as ChatGPT and Copilot to accelerate test-case generation and documentation.
What it takes
- 3–5 years in software testing covering both manual and automated methodologies.
- Strong command of functional, regression, integration, and exploratory testing.
- Experience with test and project management tools such as Jira or equivalent.
- Familiarity with scripting languages for writing custom test scripts.
- Strong analytical skills — critical thinking, edge-case identification, and structured problem-solving.
- Practical experience testing features of AI-driven SaaS products — model-backed workflows, AI-generated content, or recommendation logic.
- AI-native working practice: fluent use of AI tools to accelerate testing work and knowledgeable about AI applications in the domain.
Nice to have
- Strong documentation skills — clear, precise communication of test results and recommendations.
We respond to every application within 2 business days.