Lead Java Automation Test Engineer with AI

Lead Java Automation Test Engineer with AI

Responsibilities

• Design and evolve scalable automation frameworks in Java
• Develop resilient Selenium-based automation solutions
• Lead architecture decisions to maximize reuse, maintainability, and scalability
• Optimize execution by implementing parallel runs across browsers and platforms
• Deliver reusable libraries, utilities, and extensions to strengthen the framework
• Apply OOP concepts such as classes and objects, inheritance, polymorphism, encapsulation, and abstraction
• Implement API automation solutions for REST and SOAP services
• Validate XML and JSON payloads via serialization and deserialization approaches
• Implement authentication and authorization including Basic Authentication, Bearer Tokens, OAuth, and NTLM
• Handle session management and perform API integration testing
• Review code and enforce clean coding standards across the team
• Define and govern coding standards, branching strategies, and version control best practices
• Integrate automation suites with CI/CD pipelines using Jenkins and GitHub Actions
• Implement AI-driven solutions for automated test case generation, test script generation, and code reviews

Requirements

• Demonstrated test automation engineering experience in the 9–14 years range
• Advanced skills in Java, Selenium, and designing test automation frameworks
• Solid understanding of OOP principles and patterns including Singleton, Factory, and Decorator
• Proficiency with Page Object Model and Page Factory implementations
• Practical experience automating REST and SOAP APIs with XML and JSON validation
• Strong CI/CD and DevOps integration expertise with Jenkins and GitHub Actions
• Capability to configure static code analysis, quality gates, and automation reporting dashboards
• Hands-on use of Large Language Models such as GitHub Copilot, Claude, and Cursor
• Knowledge of Prompt Engineering, Context Window Management, and Tokenization concepts
• Familiarity with embeddings, vector databases, and semantic search applications
• Understanding of AI agents and AI-assisted engineering using tools such as OpenAI Assistants

Nice to have

• Experience developing multi-agent and agentic workflows for end-to-end testing and workflow orchestration
• Knowledge of Retrieval-Augmented Generation architectures and integration with enterprise knowledge sources
• Understanding of Model Context Protocol concepts and frameworks
• Understanding of Attention Mechanisms and Transformer-based model architectures