About MyComplianceOfficeMyComplianceOffice builds enterprise compliance and riskplatforms for global financial services clients. Our systems are distributed,highly configurable, and operate at scale—requiring quality engineeringapproaches that ensure reliability, trust, and regulatory confidence.As our platform evolves to include AI-poweredcapabilities, quality becomes even more critical: AI systems must beexplainable, predictable within defined bounds, and safe to operate inregulated environments.Role SummaryAs a Senior QA Engineer – AI Products, you act as asenior technical authority responsible for defining and implementing qualitystrategies for AI-powered features and services built withinMyComplianceOffice.In addition to governing automation frameworks at scale, youwill focus on validating AI system behaviour, including datadependencies, model outputs, non-deterministic behaviour, and production drift.You ensure that AI-driven functionality is reliable, trustworthy, andcompliant—both at release time and as models evolve in production.You are accountable not just for writing automation, but forensuring confidence in AI systems at organisational scale.Key ResponsibilitiesCore Automation & Platform Responsibilities· Design or govern modular automation frameworksadopted across multiple teams or platforms· Define and align automation goals across squadsor delivery streams· Solve complex automation challenges related todistributed systems, async flows, dynamic environments, or CI/CD scalability· Design, configure, and monitor risk- andsignal-based quality gates for high-volume pipelines· Build internal tools or integrations thatimprove execution speed, enablement, observability, and feedback loops· Define review standards, enforce qualitypractices, and enable safe test code reuse· Work closely with architects and seniorengineers to align test strategies with evolving system architecture· Drive automation and quality capability upliftthrough talks, training sessions, documentation, and pairingAI & Machine Learning Quality Responsibilities· Define testing strategies for AI andML-powered systems, including model behaviour validation and data qualitychecks· Design approaches for testing non-deterministicand probabilistic outputs, where correctness is defined by behaviour,thresholds, or confidence ranges· Validate AI features for bias, fairness,explainability, and regulatory risk· Test AI behaviour across model versions, datachanges, and feature or prompt evolution· Partner with data scientists and ML engineers toembed quality gates into training, validation, and inference pipelines· Monitor production signals to detect modeldrift, degradation, or unexpected behaviour, and feed learnings back intotest strategyRequired Skills & Experience· 5–8 years' experience in quality engineering,automation, or SDET roles· Proven experience governing or scalingautomation frameworks across teams· Strong programming skills with an architecturalmindset· Deep understanding of CI/CD systems and testexecution at scale· Experience building internal test tooling orplatform integrations· Comfortable operating in ambiguous, evolvingtechnical environments· Strong cross-team influence and mentoringcapabilityAI Systems Testing Experience (Required or StronglyPreferred)· Experience testing AI or ML-powered productsin production or pre-production environments· Understanding of the AI/ML lifecycle(training, validation, deployment, retraining)· Experience validating systems with non-deterministicoutputs· Familiarity with concepts such as falsepositives/negatives, confidence scoring, bias, and model drift· Ability to design tests where expected outcomesare behavioural, probabilistic, or risk-based, not purely binaryDesired Attributes· Systems thinker with strong judgement· Comfortable testing systems where correctnessis contextual rather than absolute· Pragmatic leader who balances standards withautonomy· Curious about how data, models, and userbehaviour interact in real-world systems· Strong ethical and risk awareness whenvalidating AI-driven decisions· Enjoys teaching, mentoring, and raisingorganisational capability· Data- and signal-driven, outcome-focused mindsetWhy This Role Is Different· Opportunity to shape how AI quality isdefined and validated in a regulated enterprise platform· Real ownership of testing strategy for AIsystems — not just test execution· Space to influence architecture, model lifecycledecisions, and release confidence· Direct collaboration with senior engineering,architecture, and data leaders