AI Automation Test Engineer (JavaScript / Java)

Zycus Infotech; Bengaluru; On-site; INR 3,600,000 - 6,000,000; Full time

Job summary

Zycus Infotech is seeking a Principal AI Automation Test Engineer to define and drive automation and AI-testing strategy across multiple products and teams in Bengaluru. You will own the technical direction, architect frameworks, mentor engineers, and pioneer AI/ML-driven testing at scale.

This hands-on leadership role requires writing and reviewing code while elevating automation reliability and velocity across the organization.

Qualifications

• Define automation strategy, test pyramid, and standards across products.
• Hands-on leadership with coding in JS/TS and Java.
• Mentor engineers and drive AI-testing adoption across org.

Responsibilities

• Architect end-to-end automation frameworks in JS/TS (Playwright, Cypress, WebdriverIO, Cucumber/BDD) and Java (Selenium WebDriver, TestNG/JUnit).
• Define org-wide automation strategy and tooling standards; guide build-vs-buy decisions.
• Lead API automation across JS/Java tooling with contract testing and service virtualization.
• Champion AI/ML-powered testing, self-healing locators, visual regression, risk-based test selection.
• Integrate LLMs/AI copilots into the engineering lifecycle and establish governance.
• Design test strategies for AI/LLM features handling non-determinism and guardrails.
• Own CI/CD test pipeline architecture and containerized/parallel execution.
• Establish flaky-test governance and reliability metrics.
• Mentor automation engineers; perform design reviews and enforce code-quality standards.
• Collaborate with engineering leadership to shift quality left in SDLC.

Skills

Automation testing

AI testing

Leadership

Mentoring

JavaScript/TypeScript

Java

CI/CD

Docker

Playwright

Cypress

Tools

Playwright

Cypress

WebdriverIO

Cucumber/BDD

Selenium WebDriver

TestNG/JUnit

Supertest

Postman

RestAssured

Jenkins

GitHub Actions

GitLab CI

Bitbucket Pipelines

Azure DevOps

Docker

Copilot

Claude

Cursor

Job description

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.