AI Automation Test Engineer (JavaScript / Java)

Job Summary

Were looking for a Principal AI Automation Test Engineer to define and drive the automation and AI-testing strategy across multiple products and teams. Youll own the technical direction of our quality engineering practice - architecting frameworks, setting standards, mentoring engineers, and pioneering the adoption of AI/ML-driven testing at scale. This is a hands-on leadership role: you still write and review code, but your impact is measured in the reliability, velocity, and maturity of automation across the org.

Key Responsibilities

• Architect and own end-to-end automation frameworks in JavaScript/TypeScript (Playwright, Cypress, WebdriverIO, Cucumber/BDD) and Java (Selenium WebDriver, TestNG/JUnit), and set the patterns other teams build on.
• Define the org-wide automation strategy - test pyramid, coverage targets, tooling standards, and the build-vs-buy decisions for testing platforms.
• Lead API automation (REST/GraphQL) architecture across JS and Java tooling (Supertest/Postman, RestAssured), including contract testing and service virtualization.
• Champion AI/ML-powered testing - self-healing locators, visual regression, and intelligent/risk-based test selection - running evaluations and driving adoption decisions across teams.
• Integrate LLMs and AI coding assistants (Copilot, Claude, Cursor, custom GPT/agent workflows) into the engineering lifecycle, and establish where they add value vs. where they introduce risk - codified as team guidance.
• Design test strategies for AI/LLM-powered product features - handling non-determinism, building eval-style and guardrail assertions, and defining quality bars for generative features.
• Own CI/CD test pipeline architecture (Jenkins, GitHub Actions, GitLab CI, Bitbucket Pipelines, Azure DevOps) and containerized/parallel execution (Docker, and orchestration at scale).
• Establish flaky-test governance - root-cause practices, quarantine policies, and reliability metrics that keep suites trustworthy across the org.
• Mentor and level up automation engineers; run design reviews, set code-quality standards (POM, centralized locators, data-driven design), and grow the teams AI-testing capability.
• Partner with engineering leadership and product to shift quality left and embed testing into the SDLC as a first-class concern.

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