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Job Description
We are looking for a highly skilled and forward-thinking
QA Automation Engineer to join our Quality Engineering team. In this role, you will design, build, and maintain robust test automation frameworks across UI, API, and BDD layers, with a strong focus on software applications supporting the chemical, scientific, or laboratory data domains.
The ideal candidate brings strong automation experience in
either Java or Python, manages our
Selenium + TestNG / PyTest infrastructure, and scales modern web automation using
Playwright integrated with Allure reporting. Crucially, you will be a key driver in modernizing our engineering practices by actively leveraging next-generation
GenAI agents and tools like Claude, GitHub Copilot, and Windsurf while maintaining robust version control workflows and orchestrating automated pipelines.
Key Responsibilities
- Framework Architecture: Design, expand, and optimize scalable UI and API automation frameworks from scratch using modern best practices.
- Modern UI Testing & Reporting: Write clean, asynchronous, and fast-executing end-to-end (E2E) automated web tests using Playwright (TypeScript/JavaScript, Python, or Java) and integrate rich, interactive Allure Reports for advanced test analytics. [1, 2, 3, 4, 5]
- Legacy UI Maintenance: Enhance, debug, and execute existing browser-automation suites written in Java + Selenium WebDriver + TestNG or Python + Selenium + PyTest.
- Advanced Git Workflow Management: Own the version control health of the test repositories by managing complex Git branching strategies (Gitflow/Feature branching), resolving merge conflicts, and executing code integrations.
- End-to-End Pipeline Management: Author, maintain, run, and continuously monitor automated test pipelines within Jenkins CI/CD integrated with AWS cloud environments to ensure reliable nightly regression feedback loops.
- GenAI-Accelerated Quality Engineering: Actively develop, refactor, and debug automation scripts by leveraging GitHub Copilot for real-time code generation and AI-native IDEs like Windsurf for autonomous agentic debugging.
- Prompt Engineering for Test Logic: Utilize advanced LLMs like Claude to generate comprehensive test cases, orchestrate edge-case scenarios from complex requirements documents, and parse complex chemical domain specifications into automation logic.
- BDD Implementation: Write human-readable test scenarios using Cucumber (Gherkin syntax) or Behave (Python equivalent) and bind them to step definitions.
- API Automation: Architect and execute reliable API integration test suites using the Karate Framework or equivalent tools.
Required Technical Skills
- Advanced Git Version Control: Proficient in code collaboration via Git, including master-level knowledge of branching, merging, rebasing, pull request (PR) reviews, and webhook integration.
- UI Automation & Analytics: Expert command over Playwright (minimum 2 years) and Selenium WebDriver (minimum 3 years), with hands-on experience setting up Allure Framework for custom dashboards, attachments, and historical test trends.
- CI/CD Pipeline Mastery: Strong working knowledge of creating, running, troubleshooting, and monitoring declarative or scripted Jenkins Pipelines (Jenkinsfile).
- GenAI Automation Tools (Must-Have): Proven, day-to-day proficiency using GitHub Copilot for rapid coding, Windsurf for agent-driven framework creation, and Claude for prompt-engineered test scenario generation.
- Core Languages: Professional proficiency in either Java (OOPs concepts, collections) or Python (data structures, asynchronous programming).
- Cloud Platforms: Practical working knowledge of AWS services (e.g., EC2, S3, RDS, Lambda, CloudWatch) as they relate to deploying and running automated test infrastructure.
- Testing Harnesses: Deep understanding of TestNG (for Java) or PyTest / Unittest (for Python) configurations.
- BDD & API Frameworks: Hands-on experience with Cucumber or Behave, alongside core proficiency in the Karate Framework for API testing.
Qualifications & Soft Skills
- Chemical Domain Knowledge (Highly Preferred): Prior experience testing applications in the Chemical, Petrochemical, Pharmaceutical, Material Sciences, or Laboratory Information Management Systems (LIMS) industries. Familiarity with domain-specific workflows, units of measurement, or scientific data visualizations is a major plus.
- AI-Native Mindset: Passionate about keeping up with the evolving GenAI ecosystem, exploring new AI coding assistants, and training junior team members on AI-assisted QA workflows.
- Problem Solver: Superior debugging skills with an emphasis on solving timing issues, dynamic locators, and network state handling.
- Pipeline Reliability Advocate: Obsessed with tracking framework flaky rates, optimizing pipeline execution time, and maintaining clean pipeline metrics.
Education & Experience Requirements
- Experience: 6 to 8 years of dedicated experience in software QA automation engineering, with dedicated commercial projects relying on both UI and API requested ecosystems.
- Education: Bachelor’s or Master's degree in Computer Science, Software Engineering, Information Technology, Chemical Engineering, Chemistry, or an equivalent technical discipline.
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