AI Test Engineer

Description

INTRODUCTION

We believe difference drives change. As inventive transformation consultants, our client blend strategic, creative and scientific capabilities, collaborating closely with customers to deliver cutting-edge solutions. Join us to drive strategic transformation tailored to our client's challenges of today and tomorrow. Informed and validated by science and data. Superpowered by creativity and design. All underpinned by technology created with purpose.

AI Model Validation

• Design and implement comprehensive testing strategies to ensure the robustness, accuracy, and reliability of AI/ML models in production.
• Develop automated test scenarios to evaluate model performance, detect data drift, and identify algorithmic bias.
• Collaborate with Data Science and MLOps teams to integrate testing into CI/CD pipelines.

Data Quality & Integration Testing

• Validate the consistency, integrity, and traceability of datasets used for training and inference.
• Support the verification of data pipelines and the integration of models into cloud environments (Azure, AWS, GCP).

Automation & Monitoring

• Build automated testing tools for ML/AI models, including regression, performance, and robustness tests.
• Implement monitoring systems to detect anomalies and ensure model stability post-deployment.

Collaboration & Documentation

• Work closely with ML engineers, developers, and project managers to ensure the quality of AI deliverables.
• Document test plans, results, and continuous improvement recommendations**.**

Requirements

Technical Skills:

• Proficient in Python and testing frameworks (e.g., PyTest, unittest).
• Familiar with AI/ML concepts and tools (e.g., scikit-learn, TensorFlow, MLflow).
• Experience with cloud platforms (Azure, AWS, GCP) and CI/CD tools (Git, Jenkins, GitLab CI).
• Understanding of data validation and model evaluation metrics.

Experience:

• 4 to 6 years of experience in software testing or QA, with at least 2 years focused on AI/ML systems.
• Hands-on experience in testing machine learning models and validating data pipelines.

Communication Skills:

• Excellent written and verbal communication skills to collaborate effectively with cross-functional teams.
• Fluent in Arabic & English, both spoken and written.

Additional Skills:

• Strong analytical and problem-solving skills.
• Team-oriented mindset with a proactive approach to quality assurance.