Quality Assurance Lead

Innovation Starts With You.

Quality Assurance Lead

Full time - Permanent Position / Remote.

Job Role:

CGS Immersive is seeking a hands-on Quality Assurance Lead to own product quality and release confidence for an AI learning platform featuring simulations, AI coaches, scenario-based training and critique. You will develop a deep understanding of how the application works and the learning outcomes it should deliver, then build and own the QA process that protects that experience as the product evolves.

You will have explicit authority to pause or block production releases when agreed quality criteria are unmet, critical defects remain unresolved, or testing evidence is insufficient. You will document the risks and work with Product and Engineering to resolve them before providing QA clearance. Quality is a shared responsibility; this role owns the QA process and independent assessment of release readiness.

The ideal candidate is diligent, analytical and highly attentive to detail, with the judgment to challenge assumptions and the follow-through to see issues through resolution. A development background is not required. Demonstrated experience building and owning a comprehensive QA process is essential. A successful candidate will need to combine AI-assisted testing with traditional, hands-on QA and sound judgment to ensure thorough coverage and confidence in each release.

Responsibilities:

• Establish and own the QA strategy, testing standards, reusable test library and release criteria. Make testing a consistent part of planning and delivery. • Create and maintain a functional map of the application, covering user roles, journeys, features and dependencies. Document expected behaviour and learning outcomes so the team has a clear, agreed definition of what good looks like. • Review Jira epics, user stories, acceptance criteria and release updates to understand the purpose of each change. Clarify gaps with Product and link requirements to test cases, results and defects. • Design and execute comprehensive functional, integration, exploratory and regression tests. Cover complete user journeys, negative scenarios, boundary conditions, permissions, interrupted sessions and recovery paths. • Analyse each release for its impact on existing functionality and dependencies. Prioritise testing by user impact and risk, and verify that fixes resolve the issue without breaking other areas. • Evaluate simulation behaviour, AI coaching, scoring and critique against learning objectives and rubrics agreed with subject matter experts. Check that feedback reflects the learner's actual performance and provides useful guidance. • Maintain representative AI test scenarios and approved reference examples. Repeat evaluations to detect unacceptable variation, lost conversational context, unsupported claims, biased or inappropriate feedback, and attempts to bypass instructions. Reassess after changes to models, prompts, learning content or configuration. • Document findings in Jira with reproducible steps, environment and version details, expected and actual results, evidence, severity and user impact. Capture prompts and conversation context for AI issues, and track defects through retesting and closure. • Provide a documented QA release assessment covering tests completed, coverage gaps, unresolved defects and remaining risks. Exercise release-blocking authority when evidence does not support confidence. • Investigate production issues with Engineering, identify why regressions escaped detection and add tests that prevent recurrence. Track production defect severity, repeat regressions and coverage of critical journeys to demonstrate improvement. • Identify repetitive tests suited to automation and work with Engineering to implement them, while maintaining strong manual and exploratory coverage.