Lead QA Engineer

QA Engineer (AI/ML)

At this time, we are unable to offer visa sponsorship for this position (H1b/OPT). Candidates must be legally authorized to work for any employer in the United States (or applicable country) on a full-time basis without the need for current or future immigration sponsorship.

Enterprise AI/ML Organization

Reports to Leader of 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 is someone who has worked closely with machine learning engineers and data scientists to ensure the quality and reliability of AI/ML models and systems. You will join a dynamic team passionate about innovation, learning, and applying cutting‑edge technologies to deliver high‑quality AI solutions.

• 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.
• Experience with the following:
  • 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.