Quality Assurance Engineer

1. AI & LLM Validation

• Non-Deterministic Testing:
• Architect automated frameworks to evaluate Generative AI outputs for hallucination, consistency, and factual accuracy against "Gold Standard" datasets.
• RAG Evaluation:
• Implement automated metrics (e.g.,

Ragas

• , faithfulness, answer relevance) to verify that Retrieval-Augmented Generation pipelines accurately cite technical and regulatory documentation.
• Prompt Regression:
• Design regression suites to monitor "prompt drift," ensuring model updates do not degrade the quality of AI-generated engineering documents.

2. Integration & System Verification

• Enterprise Integration:
• Build robust tests to validate data consistency between AI agents and critical systems (e.g.,

Sap S/4hana

• , Ariba), ensuring the integrity of Bill of Materials (BOM) and financial data.
• Performance Benchmarking:
• Design tests to validate latency and throughput for forecasting models and risk-scoring engines using tools like

Locust, JMeter, or K6

• API & Security Validation

Role-Based Access Control (RBAC)

3. Governance & Traceability

• V-Model Alignment:
• Map automated test cases to "System Requirements" to create digital evidence for formal

Verification and Validation (V&V)

• Stage Gate Compliance

• Defect Lifecycle Management

Technical Requirements

• Core Automation:
• Expert proficiency in

Python (Pytest)

Selenium/Playwright

• , Requests).
• AI Evaluation:
• Hands-on experience with LLM evaluation frameworks (e.g.,

DeepEval, TruLens

• ) and "Ground Truth" dataset management.
• Performance Engineering:
• Proficiency in crafting Performance Test Plans and implementations (Locust, K6, etc.).
• Data Validation:
• Expertise in

SQL

Great Expectations

• CI/CD & DevOps

GitLab CI/CD

• pipelines.
• Engineering Practices:
• Deep understanding of modern QE practices, including

Shift Left

Professional Qualifications

• Experience:
• 5+ years in QA Automation, with 2+ years focused on complex data-driven applications, ML models, or AI agents.
• Domain Expertise:
• Background in

Defense, Aerospace

IV&V

• processes is highly desirable.
• Analytical Mindset:
• Ability to define pass/fail criteria for probabilistic systems and communicate "Confidence Levels" to engineering leadership.
• Why AI71?
• Final Line of Defense:
• You define whether an AI agent is "trusted" to negotiate a contract or design a critical system component in a safety-critical environment.
• Tax-Free Compensation:
• A market-leading, 100% tax-free salary and benefits package.

• Full Relocation Support

• Comprehensive assistance including flights, housing support, and visa sponsorship for you and your family to Abu Dhabi.
• Pioneering Work:
• Set the global standard for how defense organizations validate intelligent systems using world-leading models.