AI Automation Quality Engineer

Summary

Harvard University is seeking an AI Automation Quality Engineer to advance intelligent quality engineering for the my.harvard portal. The role designs AI agents and Retrieval-Augmented Generation pipelines to analyze requirements and code, generate and validate test assets, and improve testing strategies. It also maintains automated testing, CI/CD integration, and quality engineering standards in collaboration with technical and product teams.

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

Skills

• Minimum of two years' post-secondary education or relevant work experience
• 3+ years building automated test frameworks for enterprise web applications
• Strong Python development experience
• Experience with Playwright, Selenium, Cypress, pytest, or similar frameworks
• Experience using Jira and Agile development processes
• Understanding of Git workflows and CI/CD pipelines
• Experience working with LLMs (OpenAI, Anthropic, Gemini, etc.), prompt engineering, and AI-assisted software development
• Strong analytical thinking and ability to translate business requirements into automated quality strategies
• Knowledge of SDLC and STLC
• Test automation and quality engineering best practices
• Experience testing Django and/or FastAPI applications
• Experience developing AI agents that generate code or software tests
• Experience integrating AI into SDLC workflows
• Knowledge of GitHub Copilot, Cursor, Claude Code, Windsurf, or similar AI development environments
• Experience with vector databases (Pinecone, pgvector, Chroma, Weaviate)
• Experience with OpenTelemetry, observability, and evaluation frameworks for AI systems
• Experience in higher education or enterprise ERP/student information systems
• Knowledge of Oracle, REST APIs, GraphQL, Docker, Kubernetes, and cloud-native development
• Experience measuring test coverage, mutation testing, and AI quality metrics
• AI/ML engineering and agentic system design
• Test automation and quality engineering best practices
• Understanding of Retrieval-Augmented Generation (RAG), embeddings, vector databases, MCP, or agent frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar
• Cross-functional collaboration with Business Analysts, developers, and product owners
• Knowledge of advanced IT project management principles (e.g. Agile) and software
• Trong analytical thinking and problem-solving
• Demonstrated team performance skills, service mindset approach, and the ability to act as a trusted advisor
• Continuous improvement and adaptability to emerging AI and testing technologies
• Understanding of Git workflows and CI/CD pipelines
• Completion of Harvard IT Academy specified foundational courses (or external equivalent) preferred

Qualifications

Must Haves

• Minimum of two years' post-secondary education or relevant work experience
• 3+ years building automated test frameworks for enterprise web applications
• Strong Python development experience
• Experience with Playwright, Selenium, Cypress, pytest, or similar frameworks
• Experience using Jira and Agile development processes
• Understanding of Git workflows and CI/CD pipelines
• Experience working with LLMs (OpenAI, Anthropic, Gemini, etc.), prompt engineering, and AI-assisted software development
• Strong analytical thinking and ability to translate business requirements into automated quality strategies
• Knowledge of SDLC and STLC
• Test automation and quality engineering best practices

Nice to Haves

• Experience testing Django and/or FastAPI applications
• Experience developing AI agents that generate code or software tests
• Experience integrating AI into SDLC workflows
• Knowledge of GitHub Copilot, Cursor, Claude Code, Windsurf, or similar AI development environments
• Experience with vector databases (Pinecone, pgvector, Chroma, Weaviate)
• Experience with OpenTelemetry, observability, and evaluation frameworks for AI systems
• Experience in higher education or enterprise ERP/student information systems
• Knowledge of Oracle, REST APIs, GraphQL, Docker, Kubernetes, and cloud-native development
• Experience measuring test coverage, mutation testing, and AI quality metrics
• AI/ML engineering and agentic system design
• Test automation and quality engineering best practices
• Understanding of Retrieval-Augmented Generation (RAG), embeddings, vector databases, MCP, or agent frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar
• Cross-functional collaboration with Business Analysts, developers, and product owners
• Knowledge of advanced IT project management principles (e.g. Agile) and software
• trong analytical thinking and problem-solving
• Demonstrated team performance skills, service mindset approach, and the ability to act as a trusted advisor
• Continuous improvement and adaptability to emerging AI and testing technologies
• Understanding of Git workflows and CI/CD pipelines
• Completion of Harvard IT Academy specified foundational courses (or external equivalent) preferred

Benefits

• Generous paid time off including parental leave
• Medical, dental, and vision health insurance coverage starting on day one
• Retirement plans with university contributions
• Wellbeing and mental health resources
• Support for families and caregivers
• Professional development opportunities including tuition assistance and reimbursement
• Commuter benefits, discounts and campus perks
• Fully remote work arrangement at a non-Harvard location, subject to working in a Harvard registered state and occasional on-site requirements at the department's discretion