Lead QA Engineer

Join to apply for the Lead QA Engineer role at Global Payments Inc.

We are unable to offer visa sponsorship for this position (H1b/OPT). Candidates must be legally authorized to work in the United States on a full‑time basis without the need for current or future immigration sponsorship.

A QA Engineer (AI/ML) role in the Enterprise AI/ML Organization, reporting to the Leader of the ML Engineering Group.

This QA Engineer position is for a hands‑on professional with experience in testing and automating AI/ML pipelines. The ideal candidate has worked closely with machine learning engineers and data scientists to ensure the quality and reliability of AI/ML models and systems.

• Develop and implement QA strategies tailored for AI/ML solutions, including models, APIs, pipelines, and agent‑based architectures.
• Create and maintain automated and manual test cases for model validation (accuracy, bias, robustness, explainability, drift).
• Collaborate with AI engineers, data scientists, and product teams to define success criteria, acceptance standards, and performance metrics.
• Validate model outputs and system behaviors against business and ethical guidelines.
• Perform regression, integration, stress, and adversarial testing of AI models and systems.
• Identify, log, and track bugs and anomalies, ensuring timely resolutions.
• Support monitoring production AI systems to detect model performance degradation (concept drift, data drift, hallucinations).
• Ensure compliance with internal AI governance standards, responsible AI principles, and regulatory requirements.
• Contribute to building automated AI testing frameworks, pipelines, and synthetic data generation systems.
• Document testing procedures, results, and quality assessments clearly and effectively.

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• Minimum 6 years of experience in quality assurance, specifically testing AI/ML applications.
• Hands‑on skills with Python and relevant AI/QA libraries (Pytest, Unittest, Great Expectations, MLflow, Deepchecks, etc.).
• Familiarity with machine learning frameworks (Tensor Flow, PyTorch, or scikit‑learn).
• Experience with test automation tools and frameworks.
• Knowledge of CI/CD tools (Jenkins, Git Lab CI, or similar).
• Experience with containerization technologies like Docker and orchestration systems like Kubernetes.
• Familiarity with version control systems like Git.
• Strong understanding of software testing methodologies and best practices.
• Excellent analytical and problem‑solving skills.
• Excellent communication and collaboration skills.

• Mid‑Senior level

• Full‑time

• Engineering and Information Technology

• Financial Services
• IT Services and IT Consulting

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