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AI QA/Testing Engineer
AI QA/Testing EngineerPozent Corporation • South Plainfield, NJ, US
AI QA / Testing Engineer

AI QA / Testing Engineer

Pozent Corporation • South Plainfield, NJ, US
1 day ago
Job type
  • Full-time
Job description

Skill Set – Model Validation, Bias / Fairness Testing, Automation

Role Overview

Ensure quality, fairness, and reliability of AI / ML models through comprehensive testing, validation, and automation. Focus on model performance, bias detection, and ethical AI practices.

Responsibilities

  • Design and execute test strategies for AI / ML models and systems
  • Perform model validation including accuracy, robustness, and performance testing
  • Conduct bias and fairness testing to identify and mitigate discriminatory outcomes
  • Develop automated testing frameworks for continuous model evaluation
  • Test data pipelines, feature engineering, and model inference systems
  • Create test cases for edge cases, adversarial inputs, and model behavior analysis
  • Monitor model drift and performance degradation in production
  • Collaborate with ML engineers and data scientists to ensure model quality standards

Requirements

  • Bachelor's degree in Computer Science, Engineering, Data Science, or related field
  • Strong experience in model validation and ML testing methodologies
  • Proven expertise in bias and fairness testing for AI systems
  • Hands-on experience with test automation frameworks and tools
  • Understanding of ML model evaluation metrics and statistical testing
  • Proficiency in Python and testing libraries (pytest, unittest)
  • Knowledge of AI ethics, responsible AI principles, and regulatory requirements
  • Experience with CI / CD pipelines and version control (Git)
  • Preferred

  • Experience testing various ML models (LLMs, computer vision, NLP, recommender systems)
  • Familiarity with fairness metrics (demographic parity, equalized odds, disparate impact)
  • Knowledge of adversarial testing and model robustness evaluation
  • Experience with A / B testing and experimental design
  • Understanding of data quality testing and validation
  • Familiarity with ML frameworks (PyTorch, TensorFlow, scikit-learn)
  • Experience with monitoring tools (MLflow, Weights & Biases, Evidently AI)
  • Knowledge of GDPR, AI Act, or other AI governance frameworks
  • J-18808-Ljbffr

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