Principal AI-Augmented Test Automation Engineer
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Job Description
Principal engineer scaling AI-native Playwright and TypeScript test automation frameworks for client engagements. Advising stakeholders on quality strategy, AI adoption, and sustainable engineering workflows.
Responsibilities:
- Define and evolve the engagement model for AI-native test automation initiatives
- Adapt and extend the universal rule and skill system for different client environments
- Advise senior stakeholders on quality strategy, SDLC improvements, automation maturity, and AI adoption
- Drive framework standardization while balancing project-specific requirements
- Review AI-generated code, architecture decisions, reports, rules, and testing artifacts
- Identify systemic issues, false positives, broken contracts, redundant logic, and inefficient implementations
- Trace issues to root causes and define corrective actions at the appropriate system layer
- Improve the reliability and quality of AI-assisted engineering workflows
- Define principles and guardrails for AI usage in test automation
- Work with AI coding agents and structured prompting approaches to optimize engineering workflows
- Improve prompts, skills, layered rules, and framework integrations
- Optimize token usage and cost efficiency while maintaining delivery quality
- Own and evolve a scalable Playwright and TypeScript automation framework
- Design and maintain page objects, reusable page-element components, fixtures, selectors, reporting, and test data management
- Establish and enforce test-design standards and architectural consistency
- Support framework portability and scalability across multiple engagements
Requirements:
- Strong hands-on experience with Playwright and TypeScript test automation architecture
- Proven experience building and evolving automation frameworks from scratch
- Deep understanding of scalable E2E testing design patterns and framework organization
- Experience with AI coding agents such as Cursor, Claude Code, or similar tools
- Strong analytical skills to validate and challenge AI-generated outputs
- Experience defining methodologies, playbooks, standards, or reusable engineering practices
- Excellent communication and stakeholder management skills
- Ability to work in customer-facing advisory and consulting environments
- Upper-Intermediate level of English
- Experience building AI-native QA or engineering workflows is a plus
- Knowledge of token optimization and AI operational economics is a plus
- Experience leading or scaling QA/automation practices is a plus
- Understanding of SDLC transformation and quality engineering strategy is a plus
- Experience in consulting or multi-client delivery environments is a plus
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