Senior Quality Assurance (AI/ML)

Oversees the end-to-end testing of AI/ML solutions, ensuring model accuracy, data integrity, and system reliability Collaborates with data scientists and engineers to develop robust quality standards and automated test frameworks.

Responsibilities

  • Design comprehensive test strategies for AI/ML products, covering data validation, model performance, and system integration
  • Develop and maintain automated testing frameworks to streamline model evaluations and regression testing
  • Collaborate with data scientists to identify edge cases, potential biases, and data-quality issues
  • Execute exploratory and stress testing on AI pipelines to confirm resiliency and scalability
  • Set up and track key QA metrics (precision, recall, F1-score) for machine learning models
  • Review model deployments in production, ensuring real-time monitoring and alerting for performance drifts
  • Lead defect triages, root-cause analyses, and resolution tracking for AI-related issues
  • Mentor junior QA engineers on AI/ML best practices, test design, and automation techniques

Required Skills

  • Bachelor’s degree in Computer Science, Engineering, or related field
  • 5+ years of QA experience with a focus on AI/ML or data-intensive systems
  • Proficiency in Python or similar languages for test automation
  • Understanding of machine learning workflows, data preprocessing, and model evaluation metrics
  • Experience with CI/CD pipelines (Jenkins, GitLab, or similar)
  • Strong analytical thinking and communication skills
  • Proven track record implementing automated tests for complex data pipelines and model deployments

Preferred Qualifications

  • Master’s degree in a technical field or specialization in AI/ML
  • Familiarity with cloud-based ML platforms (AWS Sagemaker, GCP AI Platform, Azure ML)
  • Exposure to containerization/orchestration (Docker, Kubernetes) for testing distributed AI systems
  • Certification in QA methodologies (e.g., ISTQB) or advanced machine learning frameworks

compensationAndBenefits

  • Innovation: Embrace cutting-edge tools and procedures for quality in AI/ML
  • Collaboration: Partner with data scientists, engineers, and stakeholders to deliver robust solutions
  • Integrity: Commit to unbiased model testing, ensuring ethical and reliable AI outcomes
  • Excellence: Maintain high standards of quality, performance, and efficiency
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