We are looking for a Senior Java Test Automation Engineer with strong Generative AI (GenAI) expertise to drive the evolution of test automation through AI-powered solutions. In this role, you will combine advanced Java engineering and test automation skills with hands-on experience in LLMs, AI-assisted testing, and agentic workflows to improve test coverage, execution efficiency, and software quality. You will design and enhance scalable automation frameworks, integrate AI capabilities into testing processes and CI/CD pipelines, and develop intelligent solutions for test generation, test data creation, failure analysis, and automation maintenance. You will also evaluate emerging AI technologies and collaborate with engineering teams to drive the adoption of AI across the software development lifecycle. Responsibilities Design, develop, and maintain scalable test automation frameworks using Java and industry-standard testing tools, ensuring reliability, maintainability, and high test coverage Integrate Generative AI and LLM-based capabilities into test automation workflows to support test case generation, automation script development, test data synthesis, and intelligent defect analysis Develop and integrate AI-powered agents, utilities, and APIs to automate testing activities, optimize regression testing, and streamline quality assurance processes Apply software design patterns, clean code principles, reusable components, and robust architectural practices to build maintainable automation solutions Integrate automated test suites and AI-powered testing capabilities into CI/CD pipelines, enabling continuous testing, intelligent test selection, automated failure analysis, and quality gates Evaluate the accuracy, reliability, and effectiveness of LLM-based testing solutions using appropriate evaluation methods, monitoring, and observability practices Assess emerging GenAI models, AI coding assistants, and AI-driven testing tools; develop prototypes and recommend solutions based on technical feasibility and business value Work closely with developers, QA engineers, DevOps specialists, and other stakeholders to promote engineering excellence and accelerate AI adoption across the SDLC Present technical solutions and AI-driven automation prototypes, explaining their architecture, benefits, limitations, and implementation considerations Requirements 3+ years of experience in software engineering, test automation, QA automation, or a related software development role Strong hands-on experience with Java and building automated testing solutions using frameworks and tools such as JUnit, Playwright, TestNG, Selenium, REST Assured, or equivalent technologies Experience designing and maintaining reusable automation frameworks, applying object-oriented programming, design patterns, clean code principles, and effective test automation practices Practical experience applying GenAI technologies, LLM APIs, and prompting techniques to software testing, automation engineering, or software development workflows Hands-on experience using AI tools to accelerate test design, generate or improve automation scripts, create synthetic test data, analyze test failures, or optimize regression testing Understanding of agentic workflows, tool calling, LLM evaluation, and methods for assessing the quality, reliability, and limitations of AI-generated outputs Experience integrating automated tests into CI/CD pipelines using tools such as Jenkins, GitHub Actions, GitLab CI, or equivalent platforms Knowledge of Git-based workflows, code reviews, static code analysis, debugging, and software development best practices Strong analytical and troubleshooting skills, ability to work independently, and effective communication skills for collaboration with distributed teams and clients English proficiency at an Upper-Intermediate level (B2) or higher Nice to have Experience building LLM-powered applications, agents, or workflows using frameworks such as LangChain, LangGraph, LlamaIndex, or similar technologies Knowledge of AI-powered testing tools, self-healing automation, intelligent test selection, or automated root cause analysis Experience with AI observability, tracing, evaluation frameworks, and monitoring tools for LLM-based applications Familiarity with Python or JavaScript for developing complementary automation utilities and AI integrations Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform Knowledge of Docker, Kubernetes, infrastructure monitoring, and cloud-native engineering practices Experience with embedded systems testing, hardware-related test automation, or testbed development
