AI Automation Quality Engineer

Job-Specific Responsibilities:

The AI Automation Quality Engineer leads the next generation of quality engineering for the my.harvard portal, moving beyond traditional QA to design intelligent, agent-based testing capabilities that automatically analyze requirements, understand application code, generate reusable test assets, and continuously improve HUIT’s testing strategy.

• This role combines strong software testing experience with modern AI engineering concepts, including Retrieval-Augmented Generation (RAG), large language models (LLMs), agentic workflows, and test automation frameworks. Working closely with Business Analysts, developers, and product owners, the AI Automation Quality Engineer helps build an autonomous QA platform that accelerates delivery while improving software quality.

• AI-Powered Test Generation: Design and implement AI agents that read Jira user stories, acceptance criteria, architectural documentation, and business rules to automatically generate comprehensive functional, integration, regression, accessibility, and API test scenarios

• Code-Aware Testing: Build agents capable of analyzing Django, FastAPI, and frontend codebases to understand application behavior, identify impacted components, and generate reusable test cases with high coverage

• RAG & Knowledge Engineering: Develop Retrieval-Augmented Generation (RAG) pipelines that leverage Jira, Confluence, Git repositories, existing test suites, architecture documentation, API specifications, and historical defects to improve test quality and reduce hallucinations

• Human-in-the-Loop Validation: Implement workflows where generated test scenarios are reviewed and validated by Business Analysts and Product Owners before automation code is produced

• Autonomous Test Automation: After approval, generate maintainable automated tests using Playwright, Selenium, pytest, or equivalent frameworks, validate results, and prepare commits or pull requests for inclusion in the shared test repository

• Continuous Learning: Capture review feedback, production defects, and failed test patterns to improve prompts, retrieval strategies, reusable testing patterns, and agent performance over time

• Quality Engineering: Maintain regression suites, API tests, UI automation, performance validation, and CI/CD integration while ensuring generated tests remain reliable and maintainable

• Collaboration: Partner with developers, architects, BAs, and DevOps engineers to define AI-driven quality engineering standards and governance