What can I expect to do in this job?
This isn’t an exhaustive list, but things you can expect to be involved with include:
· Build and document system knowledge in Confluence in the Quasar-prescribed format — process diagrams, mind maps and requirements that make system components, end-to-end data flows and dependencies clear to all team members.
· Own requirement traceability for assigned systems – baseline and re-baseline traceability matrices, keep coverage accurate through ConnectALL mappings, and support sign-off of requirement acceptance for Quasar.
· Develop and execute test strategies for AI-enabled functionality (LLM features, ML models, RAG pipelines), with acceptance criteria and test oracles suited to non-deterministic systems.
· Evaluate AI outputs for accuracy, relevance, consistency and safety – identifying hallucinations, bias and fairness issues, and managing them through standard defect management.
· Validate AI/ML performance using appropriate metrics (accuracy, precision, recall, F1), assure training and evaluation data quality, and monitor for data drift.
· Conduct adversarial and robustness testing on AI features – red-teaming, prompt injection, edge cases – and validate guardrails and human-in-the-loop controls.
· Contribute to AI governance evidence – risk assessments, explainability documentation, model validation records, and post-deployment monitoring of AI-enabled services.
· Create and deliver all key QA deliverables – test plans, strategies, scope, test objective matrices, test cases, reports and defect management – across waterfall and agile projects.
· Maintain and build functional and non-functional testing using approved automation tools – Selenium, Robot Framework, Python, ReadyAPI, SauceLabs, LoadRunner – automating regression to improve time to market.
· Manage automation scripts and code bases through GitHub and GitLab, supporting testing in a CI/CD and DevOps model, and use AI tools to augment test generation, coverage and productivity.
· Ensure supplier and third-party test delivery (including AI features) is in line with RDG policy and standards, working collaboratively with vendors for release and product quality.
· Report daily testing status to the Test Manager and stakeholders, escalate delays proactively, and share knowledge across the team on technical and AI testing subjects.