G42
Lead QA Engineer
- Permanent
- Abu Dhabi, United Arab Emirates
- Experience 10 - 15 yrs
Job expiry date: 24/11/2026
Job overview
Date posted
10/10/2026
Location
Abu Dhabi, United Arab Emirates
Salary
Undisclosed
Compensation
Comprehensive package
Experience
10 - 15 yrs
Seniority
Senior & Lead
Qualification
Bachelors degree
Expiration date
24/11/2026
Job description
AI-enabled financial applications do not always give the same answer twice, and this Abu Dhabi QA lead at Inception42 is responsible for proving they still behave correctly. End-to-end QA strategy covers functional, integration, regression, usability, accessibility and performance testing, with risk-based acceptance criteria for each business workflow. AI behaviour gets particular scrutiny, from response relevance and factual consistency to multi-turn context, RAG retrieval, tool execution and handling of unsupported requests. Safeguards against prompt injection, sensitive data disclosure and cross-tenant access are tested alongside fallback behaviour when AI services fail or time out. Automation frameworks for UI, API, integration and data validation feed smoke and regression suites into CI/CD pipelines, using tools such as Playwright, Selenium, Cypress, REST Assured and Postman. Financial calculations, reconciliations, reporting and audit trails also need validating. QA engineers across internal and vendor teams look to you for mentoring and code review, and Azure DevOps holds traceability, test evidence and quality reporting. Release readiness calls, with QA sign-off ahead of Product sign-off, rest with you. At least 10 years of QA experience, including 3 years of technical leadership, hands-on AI application testing, strong SQL and a Bachelor's degree in Computer Science or Engineering are required.
Required skills
Key responsibilities
- Own the end-to-end QA strategy, test planning and execution for AI-enabled applications
- Mentor QA engineers and review test designs and automation code across internal and vendor teams
- Define risk-based coverage and acceptance criteria for business workflows, including AI-assisted interactions
- Validate AI behaviour for relevance, factual consistency, instruction adherence and handling of unsupported requests
- Test multi-turn conversational experiences, context retention, session isolation and error recovery
- Check document ingestion and retrieval workflows for relevant sources, accurate references and access permissions
- Exercise AI-driven orchestration and tool integrations for correct actions, authorisation boundaries and duplicate prevention
- Probe safeguards against prompt injection, sensitive information disclosure and cross-tenant access
- Confirm graceful handling of unavailable AI services, timeouts and dependency failures
- Build scalable automation frameworks for UI, API, integration and data validation testing
- Maintain reusable business scenarios, test data and regression suites for prompt, configuration and release changes
- Integrate automated smoke and regression testing into CI/CD pipelines
- Audit financial calculations, reconciliations, reporting and audit trails
- Assess responsiveness, concurrency and stability under expected and peak workloads
- Collaborate with Security and Compliance teams to verify application controls
- Drive defect triage and root-cause analysis, turning production findings into better coverage
- Track requirements traceability, test evidence, defects and quality reporting in Azure DevOps
- Communicate release readiness, open defects and residual risks ahead of QA and Product sign-off
Experience & skills
- Hold a Bachelor's degree in Computer Science, Engineering or a related field
- Bring at least 10 years of QA experience, including 3 years of technical leadership in enterprise environments
- Show hands-on experience testing AI-enabled applications such as conversational assistants, enterprise search or document processing
- Demonstrate strong expertise in functional testing, exploratory testing, test design and automation engineering
- Offer experience building UI, API and end-to-end automation frameworks
- Understand prompts, conversational context, RAG and AI-driven tool execution
- Design repeatable acceptance checks for applications with variable responses
- Apply strong SQL skills to validate data flows, transformations and reporting
- Know financial workflows, access controls and audit requirements
- Work within Agile delivery using Git and CI/CD on cloud-based applications
- Explain quality risks clearly to support release decisions
- Coordinate testing across multidisciplinary teams