Principal AI-Augmented Test Automation Engineer

About the role

Process & Solution Establishment 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 Analytical Review & AI Output Validation Review AI-generated outputs, including code, architecture decisions, reports, rules, and testing artifacts Quickly identify systemic issues, false positives, broken contracts, redundant logic, or inefficient implementations Trace issues to their root causes and define corrective actions at the right system layer Improve the reliability and quality of AI-assisted engineering workflows AI-Augmented Testing Operations Define principles and guardrails for effective AI usage in test automation Work with AI coding agents and structured prompting approaches to optimize engineering workflows Continuously improve prompts, skills, layered rules, and framework integrations Optimize token usage and cost efficiency while maintaining delivery quality Test Automation Architecture Own and evolve a scalable Playwright + TypeScript automation framework Design and maintain page objects, reusable page-element components, fixtures, selectors, reporting, and test data management Establish and enforce strong test-design standards and architectural consistency Support portability and scalability of the framework across multiple engagements 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 working with AI coding agents such as Cursor, Claude Code, or similar tools Strong analytical skills with the ability 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 WILL BE A PLUS: Experience building AI-native QA or engineering workflows Knowledge of token optimization and AI operational economics Experience leading or scaling QA/automation practices Understanding of SDLC transformation and quality engineering strategy Experience working in consulting or multi-client delivery environments

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