Senior Quality Assurance Engineer

Key Responsibilities

• Design and implement comprehensive test plans and test cases for AI-powered applications, APIs, and platform features.
• Develop automated testing frameworks for AI models, data pipelines, and software features, primarily leveraging Python.
• Evaluate AI model outputs for correctness, fairness, robustness, and consistency across various tasks and datasets.
• Collaborate with AI researchers, data scientists, and engineers to define testing metrics and acceptance criteria for AI models.
• Conduct manual and exploratory testing where automation is insufficient, especially for AI feature behavior.
• Perform end-to-end testing across distributed systems, APIs, databases, web applications, and model inference pipelines.
• Implement performance testing for both AI inference and software systems to ensure responsiveness and scalability.
• Develop continuous integration (CI) and continuous deployment (CD) pipelines to ensure reliable and repeatable testing processes.
• Track, analyze, and report defects and quality metrics to stakeholders, driving root cause analysis and resolution.
• Contribute to internal quality standards, best practices, and documentation to strengthen the QA function within IFM.

Academic Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, AI, or a related technical field required.
• Master’s degree or equivalent experience in AI systems, software testing, or quality assurance preferred.

Professional Experience - Required

• Extensive experience in software quality assurance, test automation, and validation of complex software systems.
• Strong proficiency in Python, with hands-on experience in test automation frameworks such as PyTest or similar.
• Understanding of AI/ML model testing challenges, including non-deterministic outputs, dataset biases, and model evaluation metrics.
• Experience testing distributed systems, RESTful APIs, cloud-native applications, and microservices architectures.
• Familiarity with CI/CD pipelines using tools such as Jenkins, GitLab CI, or GitHub Actions.
• Strong analytical skills, attention to detail, and ability to identify edge cases and failure modes.
• Excellent communication skills, with the ability to collaborate across multi-disciplinary teams.

Professional Experience - Preferred

• Experience testing AI models (LLMs, computer vision, multi-modal models) and AI-enhanced software features.
• Proficiency with AI/ML libraries and frameworks such as TensorFlow, PyTorch, or Hugging Face for model validation.
• Familiarity with data quality assurance, dataset validation, and annotation workflows.
• Knowledge of cloud infrastructure platforms (AWS, Azure, GCP) and containerized deployments (Docker, Kubernetes).
• Arabic language proficiency to assist in linguistic, data, or region-specific validation scenarios.
• Contributions to open-source QA tools or AI testing frameworks.