Analyst II, AI QA Engineer (R-18851)
Eyeota
Key Responsibilities:
- Design, develop, and execute test plans and test cases to validate the functionality, performance, and reliability of AI/ML systems
- Collaborate with the development team to understand model requirements and identify potential areas of weakness
- Perform data validation to ensure the quality, integrity, and representativeness of the datasets used for training and testing
- Test for and identify potential biases in AI models to ensure fairness and ethical compliance
- Analyze and report on model performance using key metrics (e.g., accuracy, precision, recall, F1-score)
- Ensure accuracy, consistency, tool-call reliability, trace quality, and guardrail adherence
- Assess regression, functionality, performance, safety, and hallucination risks
- Document and track defects and work with developers to facilitate their resolution
- Assist in the development and maintenance of automated testing frameworks for AI applications
- Conduct exploratory testing to discover edge cases and unexpected behaviors in our AI systems
- Stay current with the latest advancements in AI testing methodologies and tools
- Produce test plans, scenario libraries, coverage reports, and defect logs
- Deliver insights to Data Science & Engineering teams to improve reliability
Key Requirements:
- Bachelor's degree in computer science, Engineering, Statistics, or a related technical field
- Solid understanding of software testing principles and the software development lifecycle (SDLC)
- Basic programming proficiency, preferably in Python, for writing test scripts and analyzing data
- A foundational understanding of machine learning concepts (e.g., supervised/unsupervised learning, classification, regression)
- Strong analytical and problem-solving skills with an exceptional eye for detail
- Excellent communication and collaboration skills, with the ability to articulate technical issues clearly
- Prior internship or project experience in software testing or a data-related field
- Familiarity with machine learning libraries and frameworks such as TensorFlow, PyTorch, or Scikit-learn
- Experience with testing tools and frameworks like PyTest, Selenium, or Postma
- Knowledge of SQL for querying and validating data
- Familiarity with version control systems like Git
- A genuine passion for artificial intelligence and a desire to learn and grow in the field

