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KCRise Fund
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AI QA Engineer

Sailes

Sailes

Software Engineering, Data Science, Quality Assurance
United States · Alpharetta, GA, USA · Georgia
Posted on Mar 9, 2026
About The Role

We are looking for a highly skilled AI QA Engineer to join our team and ensure the quality, reliability, and performance of AI/ML-driven applications. The ideal candidate will have a strong background in software testing, a solid understanding of AI/ML workflows, and experience with automated testing frameworks. This role will focus on validating AI models, testing data pipelines, and ensuring the overall robustness of intelligent systems before deployment.

Key Responsibilities

  • Design, develop, and execute test plans and test cases for AI/ML-based products.
  • Validate AI models for accuracy, fairness, robustness, and performance.
  • Test data pipelines, feature engineering processes, and model integration with production systems.
  • Develop automated test scripts for model APIs, training pipelines, and inference workflows.
  • Collaborate with data scientists, ML engineers, and software developers to identify and resolve quality issues.
  • Implement monitoring and alerting mechanisms for deployed AI systems.
  • Conduct regression testing to ensure changes do not degrade model performance.
  • Ensure compliance with data privacy, security, and responsible AI standards.

Required Qualifications

  • Bachelor's or Master's degree in Computer Science, Engineering, or related field.
  • 3+ years of experience in software quality assurance, with at least 1 year in AI/ML testing.
  • Strong knowledge of QA methodologies, tools, and processes.
  • Hands-on experience with automated testing frameworks (e.g., PyTest, Selenium, Robot Framework).
  • Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
  • Experience testing RESTful APIs and microservices.
  • Proficiency in Python (preferred), Java, or other programming languages.
  • Strong problem-solving skills and attention to detail.

Preferred Qualifications

  • Experience with ML model validation, monitoring, and drift detection.
  • Knowledge of MLOps tools (MLflow, Kubeflow, Airflow, CI/CD for ML).
  • Understanding of bias, fairness, and explainability in AI systems.
  • Exposure to cloud platforms (AWS, GCP, Azure) for AI/ML deployment.
  • ISTQB or equivalent certification in QA.

What We Offer

  • Competitive salary and benefits package.
  • Opportunity to work on cutting-edge AI products.
  • Collaborative, innovative, and growth-focused work environment.
  • Professional development and learning opportunities.