AI Quality Assurance Engineer

Tawantech; Riyadh; Sur place; SAR 180 000 - 260 000; Plein temps

Résumé du poste

Tawantech in Riyadh seeks an AI Quality Assurance Engineer to ensure the quality, reliability, accuracy, and performance of AI/ML solutions across the development lifecycle. The role develops and implements testing strategies for AI models, data pipelines, AI applications, and production deployments while ensuring solutions meet business, technical, security, and regulatory requirements.

Collaborate with Data Scientists, AI Engineers, Data Engineers, and Business stakeholders to design tests,

Qualifications

• Bachelor’s degree in Computer Science, Data Science, AI, Engineering, or a related field.
• 5+ years of experience in QA, AI/ML testing, software testing, or data quality.
• Strong understanding of machine learning and AI lifecycle processes.
• Experience with Python and automated testing frameworks.
• Knowledge of SQL and data validation techniques.
• Experience with APIs, cloud platforms, CI/CD, and version control.
• Understanding of ML model evaluation metrics and model monitoring.
• Strong analytical, problem-solving, and communication skills.

Responsabilités

• Develop and execute comprehensive QA and testing strategies for AI/ML solutions.
• Validate AI models for accuracy, consistency, robustness, and performance.
• Design test cases for ML models, AI apps, APIs, and data pipelines.
• Perform data quality, data validation, and data integrity testing.
• Test AI outputs for accuracy, bias, hallucination, reliability, and consistency where applicable.
• Conduct regression, integration, functional, performance, and automated testing.
• Establish quality gates and acceptance criteria for AI model deployment.
• Monitor model performance and identify model/data drift in production.
• Collaborate with Data Scientists, AI Engineers, Data Engineers, and Business stakeholders.
• Automate testing processes and integrate QA into CI/CD pipelines.
• Document defects, test results, risks, and remediation activities.
• Support model governance, auditability, and compliance requirements.

Connaissances

QA testing

AI/ML testing

Data quality

Data validation

Model monitoring

Python

SQL

CI/CD

APIs

communication

Formation

Bachelor's degree in Computer Science, Data Science, AI, Engineering, or related field

Outils

Python

SQL

APIs

CI/CD

Cloud platforms

Version control

Description du poste

Job Title: AI Quality Assurance Engineer
Experience: 5+ Years
Job Type: Full-Time

Job Summary
Responsible for ensuring the quality, reliability, accuracy, and performance of AI/ML solutions throughout the development lifecycle. The role develops and implements testing strategies for AI models, data pipelines, AI applications, and production deployments while ensuring solutions meet business, technical, security, and regulatory requirements.

Key Responsibilities

• Develop and execute comprehensive QA and testing strategies for AI/ML solutions.
• Validate AI models for accuracy, consistency, robustness, and performance.
• Design test cases for machine learning models, AI applications, APIs, and data pipelines.
• Perform data quality, data validation, and data integrity testing.
• Test AI outputs for accuracy, bias, hallucination, reliability, and consistency where applicable.
• Conduct regression, integration, functional, performance, and automated testing.
• Establish quality gates and acceptance criteria for AI model deployment.
• Monitor model performance and identify model/data drift in production.
• Collaborate with Data Scientists, AI Engineers, Data Engineers, and Business stakeholders.
• Automate testing processes and integrate QA activities into CI/CD pipelines.
• Document defects, test results, risks, and remediation activities.
• Support model governance, auditability, and compliance requirements.

• Bachelor’s degree in Computer Science, Data Science, AI, Engineering, or a related field.
• 5+ years of experience in QA, AI/ML testing, software testing, or data quality.
• Strong understanding of machine learning and AI lifecycle processes.
• Experience with Python and automated testing frameworks.
• Knowledge of SQL and data validation techniques.
• Experience with APIs, cloud platforms, CI/CD, and version control.
• Understanding of ML model evaluation metrics and model monitoring.
• Strong analytical, problem-solving, and communication skills.