Principal AI-Augmented Test Automation Engineer

Sigma Software

About The Position

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.

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 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

Nice To Haves

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

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 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
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
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