1. 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
2. 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
3. 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
4. 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
Are you ready to lead the future of AI-powered quality engineering? We are looking for a Principal AI-Augmented Test Automation Engineer to co-own and scale our AI-native E2E test automation offering together with the Practice Lead.
This is not a traditional automation role focused only on implementation. You will shape the engagement model, establish scalable testing processes, define AI-driven quality engineering principles, and evolve a reusable Playwright + TypeScript framework used across multiple client environments.
You will work directly with senior stakeholders, helping organizations adopt AI-augmented testing practices while ensuring technical quality, operational efficiency, and sustainable delivery models.
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