AI Quality Assurance Engineer
Role Overview
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 Skills for This Role
Full Job Posting
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.
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.
- 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.
About tawantech
Saudi private fintech services provider delivering system integration, payments, cloud, cybersecurity, and financial technology solutions to organizations.
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