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Salary: USD 78,300 - 113,550 / annual
Moody's is seeking an AI QA Engineer to support quality engineering efforts across an Agile team. You will execute software and AI product testing, document findings, and contribute to continuous improvement of QA processes.
Key responsibilities include:
- Perform manual, exploratory, regression, API, and integration testing within an Agile team environment
- Test software features that include AI or large language model components, reviewing outputs for accuracy, relevance, completeness, consistency, and instruction-following
- Create basic test scenarios, prompts, and test data for evaluating AI behavior and identifying issues such as hallucinations, incomplete responses, and inconsistent results
- Apply basic prompt engineering and context engineering techniques during testing activities
- Use AI-assisted tools such as Codex, Claude Code, and similar AI command-line interface tools under senior team member guidance
- Assist with existing AI evaluation harnesses, test scripts, and regression checks to support quality validation efforts
- Document defects with clear steps to reproduce, expected results, and actual results, and collaborate with engineers, developers, and product owners to investigate and resolve issues
- Contribute to test documentation, reusable test cases, and continuous improvement of quality assurance processes and standards
You will join the Quality Engineering team, which is responsible for building reliable, high-quality software and AI-enabled products. This role offers a meaningful opportunity to develop practical, hands-on skills at the intersection of traditional quality assurance and emerging artificial intelligence technologies.
REQUIREMENTS:
- Bachelor's degree in Computer Science, Software Engineering, or a related field
- Basic understanding of software testing concepts, including manual, exploratory, regression, API, and integration testing methodologies
- Basic programming or scripting knowledge, preferably in Python, with familiarity in APIs, databases, JSON, version control, and defect tracking tools
- Basic understanding of AI concepts, large language models, and common AI limitations such as hallucinations, incomplete responses, and inconsistent outputs
- Interest in prompt engineering, context engineering, and the evaluation of AI-enabled products
- Ability to document defects clearly with steps to reproduce, expected results, and actual results
- Strong analytical, communication, documentation, and problem-solving skills with the ability to collaborate across engineering and product teams
- Basic understanding of artificial intelligence concepts, with curiosity and enthusiasm for learning how AI tools can be used to improve processes and drive efficiency
- Interest in exploring AI systems and a willingness to develop awareness of responsible AI practices, including risk management and ethical use
