AI Quality Assurance Engineer

1 day ago

India beBeeGenerativeai Full time US$ 15,00,000 - US$ 20,00,000

Generative AI Quality Assurance Specialist

This role is crucial in ensuring the accuracy, safety, and performance of AI-driven applications, particularly in Generative AI.

Key Responsibilities:
  • Design, Develop, and Execute Comprehensive Test Plans: Create thorough test plans for Generative AI models and applications to ensure their quality and reliability.
  • Validate AI-Generated Outputs: Assess AI-generated outputs for accuracy, coherence, bias, ethical considerations, and alignment with business requirements.
  • Develop and Maintain Automated Testing Frameworks: Build and maintain scalable and efficient automated testing frameworks for AI-based applications.
  • Perform Adversarial Testing and Security Assessment: Identify vulnerabilities in AI systems through adversarial testing and security assessment.
  • Collaborate with Cross-Functional Teams: Work with AI/ML engineers, product managers, and developers to refine model performance and mitigate errors.
  • Monitor AI Model Drift and Performance: Ensure consistent performance across different inputs and datasets by monitoring AI model drift.
  • Conduct Performance Testing: Evaluate response times, scalability, and reliability of AI systems through performance testing.
  • Document Test Cases and Results: Document test cases, test results, and defects while ensuring compliance with AI governance and ethical guidelines.
Requirements:
  • Bachelor's or Master's Degree in Computer Science or Engineering: Hold a degree in Computer Science, Engineering, or a related field.
  • 3+ Years of Experience in Quality Assurance: Possess at least 3 years of experience in Quality Assurance, with at least 1-2 years in AI/ML testing or Generative AI applications.
  • Strong Understanding of AI/ML Concepts: Have a strong understanding of AI/ML concepts, NLP models, LLMs, and deep learning architectures.
  • Experience with AI Testing Tools: Be familiar with AI testing tools such as LangTest, DeepChecks, Trulens, or custom AI evaluation frameworks.
  • Proficiency in Test Automation: Possess proficiency in test automation using Python, Selenium, PyTest, or similar frameworks.
  • Familiarity with Model Evaluation Metrics: Be familiar with model evaluation metrics such as BLEU, ROUGE, perplexity, and precision-recall for AI-generated content.
  • Knowledge of Bias Detection and Adversarial Testing: Understand bias detection and adversarial testing principles for AI systems.
  • Experience Working with APIs and Cloud Platforms: Be familiar with working with APIs, cloud platforms (AWS, Azure, GCP), and MLOps practices.
  • Strong Analytical and Problem-Solving Skills: Possess strong analytical and problem-solving skills with attention to detail.
  • Excellent Communication and Collaboration Skills: Demonstrate excellent communication and collaboration skills to work effectively in cross-functional teams.

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