Inception
42, a G42 company, is the region’s leading innovator of AI-powered domain-specific as well as industry-agnostic products, built on a rich heritage of research and development. Within the G42 ecosystem, Inception functions as the core intelligence layer – transforming data and compute infrastructure into real-world, applied AI solutions. Beyond its commercial endeavors, Inception is committed to creating positive societal impact. For more information, please visit www.inception
42.ai
We are seeking a Lead Engineer – AI Application QA to define and drive the quality assurance strategy for AI-enabled financial applications and enterprise platforms. This role combines technical leadership with hands-on testing across application functionality, AI-powered user experiences, APIs, data flows, and integrated business workflows. You will lead manual and automated testing practices, establish measurable acceptance criteria, and embed continuous testing throughout the software delivery lifecycle.
Working closely with Engineering, Product, Architecture, and AI teams, you will ensure that applications deliver reliable, secure, and contextually appropriate outcomes that meet business requirements.
- Own the end-to-end QA strategy, test planning, and execution for AI-enabled applications, covering functional, integration, regression, usability, accessibility, and performance testing.
- Lead and mentor QA engineers, review test designs and automation code, and maintain consistent testing standards across internal and vendor teams.
- Define risk-based coverage and acceptance criteria for business workflows, including AI-assisted interactions and their downstream actions.
- Validate AI application behavior against approved scenarios, checking response relevance, factual consistency with authorized source content, instruction adherence, and appropriate handling of unsupported requests.
- Test conversational experiences, including multi-turn interactions, context retention, session isolation, ambiguous inputs, and recovery from errors.
- Validate document ingestion and retrieval workflows, ensuring responses use relevant source content, provide accurate references where required, and respect user access permissions.
- Test AI-driven orchestration and tool integrations, including correct action execution, confirmation steps, authorization boundaries, retries, and prevention of duplicate or unintended actions.
- Validate application safeguards against prompt injection, sensitive information disclosure, and unauthorized access across users or tenants.
- Verify graceful handling of unavailable AI services, timeouts, incomplete responses, and dependency failures, including fallback behavior and user-facing messages.
- Build and maintain scalable automation frameworks for UI, API, integration, and data validation testing. Maintain reusable business scenarios, representative test data, and application regression suites to assess the impact of changes to prompts, configurations, integrations, and application releases.
- Integrate automated smoke and regression testing into CI/CD pipelines, supporting daily regression and timely feedback. Validate financial calculations, reconciliations, reporting, and audit trails within AI-assisted and conventional application workflows. Assess end-to-end responsiveness, concurrent usage, and application stability under expected and peak workloads.
- Collaborate with Security and Compliance teams to verify application controls against applicable requirements. Drive defect triage and root-cause analysis, translating production findings into improved test coverage.
- Maintain requirements traceability, test evidence, defects, and quality reporting in Azure Dev Ops. Communicate…
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