QA Automation Engineer

01-06-2026

2.4K

QA Automation Engineer (JavaScript / TypeScript + Performance + AI);
Role Overview;
We are seeking a strong QA Automation Engineer with solid JavaScript/TypeScript expertise, hands-on performance testing experience, and practical use of AI in engineering workflows;
This role goes beyond writing UI tests — you will contribute to automation architecture, regression reliability, performance validation, and CI/CD quality gates.

Key Responsibilities:
Design, develop, and maintain scalable Web and API automation frameworks using JavaScript/TypeScript.
Build and optimize reliable UI and REST API test suites using modern tools (Playwright, Cypress, WebdriverIO, etc.), ensuring maintainability and low flakiness.
Integrate automated tests into CI/CD pipelines, improving regression speed, stability, and failure diagnostics.
Design and execute load, stress, and endurance tests using tools such as k6 or JMeter, applying realistic load models (RPS, concurrency, ramp-up, spike scenarios).
Analyze automation and performance results, identify bottlenecks, and collaborate with engineering teams to improve system reliability and scalability.
Leverage AI tools responsibly to generate, refactor, and optimize test code, while critically reviewing and validating AI-generated outputs for correctness, security, and maintainability.

Required Qualifications:
Strong JavaScript and/or TypeScript knowledge.
Experience with modern automation frameworks (Playwright, Cypress, WebdriverIO, or similar).
Solid REST API automation experience.
Hands-on performance testing experience (k6, JMeter, or similar).
Understanding of load modeling concepts (RPS, concurrency, ramp-up strategies, spike testing).
Experience integrating automation into CI/CD pipelines.
Ability to critically review and validate AI-generated code.
Strong debugging and root cause analysis skills.

What Success Looks Like:
Stable, scalable automation framework integrated into CI/CD.
Reduced regression time and increased release confidence.
Clear performance baselines and measurable system improvements.
Responsible and effective use of AI to increase productivity without compromising quality.