Lead Java Automation Test Engineer with AI

We are hiring a Lead Java Automation Test Engineer with AI to lead Java automation frameworks, API testing, and CI/CD quality automation. You will mentor teams, standardize clean engineering practices, and use Generative AI, Agentic AI, and LLM tooling like GitHub Copilot and Claude to accelerate test creation and reviews.

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