Engineer - ML QA

Inception42, 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.inception42.ai

Overview:

We are looking for a QA Engineer to validate AI-enabled financial applications and enterprise platforms across user experiences, APIs, data flows, and integrated business workflows. You will design and execute manual and automated tests, turn product requirements into measurable acceptance criteria, and provide clear evidence of product quality throughout the software delivery lifecycle.

You will work closely with Engineering, Product, Architecture, AI, Security, and Compliance teams in a delivery environment where application behavior can span conventional software and probabilistic AI components. The role requires disciplined testing, strong technical judgment, and ownership of defects from discovery through verification.

Responsibilities:

• Design and execute risk-based test plans for functional, integration, regression, usability, accessibility, and performance testing.

• Translate product requirements and business workflows into clear acceptance criteria, test scenarios, and traceable test evidence.

• Validate AI application behavior against approved scenarios, including response relevance, consistency with authorized source content, instruction adherence, and appropriate handling of unsupported requests.

• Test conversational experiences across multi-turn interactions, context retention, session isolation, ambiguous inputs, and recovery from errors.

• Validate document ingestion and retrieval workflows, including source relevance, reference accuracy, data freshness, and user access permissions.

• Test AI-driven orchestration and tool integrations for correct action execution, confirmation steps, authorization boundaries, retries, and prevention of duplicate or unintended actions.

• Verify safeguards against prompt injection, sensitive information disclosure, and unauthorized access across users and tenants.

• Assess behavior during AI service outages, timeouts, incomplete responses, and dependency failures, including fallback paths and user-facing messages.

• Build and maintain reliable automation for UI, API, integration, and data validation testing using the team’s established frameworks.

• Maintain reusable business scenarios, representative test data, and regression suites that cover changes to prompts, configurations, integrations, and application releases.

• Integrate automated smoke and regression tests into CI/CD pipelines to provide timely, actionable feedback.

• Validate financial calculations, reconciliations, reporting, access controls, and audit trails across AI-assisted and conventional workflows.

• Evaluate end-to-end responsiveness, concurrent usage, and application stability under expected and peak workloads.

• Own defect documentation, triage, retesting, and root-cause collaboration, and convert production findings into stronger test coverage.

• Maintain requirements traceability, test results, defects, and quality reporting in Azure DevOps, and communicate release risks with clear supporting evidence.

Qualifications:

Strong software quality fundamentals, including test design, exploratory testing, defect analysis, and risk-based coverage.

• Experience testing production web or cloud applications across UI, API, integration, and end-to-end workflows.

• Hands-on experience building or maintaining automated tests with tools such as Playwright, Selenium, Cypress, REST Assured, Postman, or comparable frameworks.

• Working proficiency in at least one programming language used for test automation, such as Python, TypeScript, or Java.

• Ability to validate REST APIs, authentication and authorization controls, service integrations, and failure behavior.

• Strong SQL skills and experience verifying data flows, transformations, calculations, and reporting outputs.

• Familiarity with Git, CI/CD pipelines, Agile delivery, and cloud-based application environments.

• Understanding of AI application concepts such as prompts, conversational context, retrieval-augmented generation, and tool execution, or the ability to learn them quickly.

• Ability to design repeatable checks for variable system responses using clear business rules and expected outcomes.

• Strong debugging and communication skills, with the judgment to distinguish blocking defects from acceptable residual risk.

• An ownership mindset and the ability to execute across ambiguity while collaborating closely with engineers, product managers, and domain specialists.

• Experience testing conversational AI, enterprise search, document processing, RAG applications, or AI-driven workflows.

• Exposure to Azure App Service, Azure Functions, Azure Kubernetes Service, or Azure Data Factory.

• Experience with performance, security, accessibility, or multi-tenant access-control testing.

• Knowledge of financial systems, reconciliation, compliance controls, reporting, or audit requirements.

• Familiarity with observability tools and production quality analysis.