Job Description
📍 Location
Chennai, India
🏢 Company
Sybrant Technologies
🧑💻 Role
AI-Powered Test Automation Engineer
🧠 Experience
1–2+ Years
🎓 Qualification
B.Com / M.Com / BBA
🗓️ Posted On
April 15, 2025
📅 Application Deadline
November 30, 2025
💼 Employment Type
Full-time, Onsite
🌟 About Sybrant Technologies
Sybrant Technologies is at the forefront of digital transformation, delivering AI-powered software development, data services, testing, lease management, and KPO solutions. As a PreludeSys Group Company, Sybrant combines technical expertise with innovative solutions to help clients achieve their business objectives efficiently.
🛠️ Role Overview
As an AI-Powered Test Automation Engineer, you will design and implement AI-augmented test automation frameworks to enhance testing efficiency, coverage, and accuracy. This role involves integrating AI/ML models into test automation processes, utilizing advanced tools and technologies to optimize software quality assurance.
✅ Required Skills and Qualifications
Test Automation Expertise:
Strong experience with test automation frameworks such as Selenium, Pytest, or Robot Framework.
Programming Skills:
Proficiency in Python or Java for AI testing integrations.
AI Testing Tools:
Familiarity with AI testing tools like Testim, Mabl, and Applitools.
Machine Learning Concepts:
Basic understanding of machine learning concepts, including model training and data labeling for testing.
💡 Preferred Qualifications (Good to Have)
LLM-Based Test Generation:
Experience with LLM-based test generation (e.g., using ChatGPT for test scripts).
Anomaly Detection:
Knowledge of anomaly detection in test results.
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Responsibilities
🎯 Key Responsibilities
Test Automation Development:
Develop AI-enhanced test automation scripts using Python or Java with tools like Selenium, Playwright, or Cypress.
Implement self-healing tests that auto-correct locators.
Detect flaky tests by predicting unstable test cases.
Generate test cases using Large Language Models (LLMs) such as GPT-4 or Gemini.
Visual Testing:
Utilize visual testing tools like Applitools and Percy with AI for UI validation.
Predictive Analytics:
Optimize test suites by prioritizing high-risk areas using predictive analytics.
CI/CD Integration:
Work with CI/CD pipelines (e.g., Jenkins, GitHub Actions) for AI-driven test execution.
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