Lead Java Automation Test Engineer with AI

We are searching for a Lead Java Automation Test Engineer with AI to lead Java/Selenium automation, API coverage, and DevOps-aligned test delivery. You will set framework direction, improve parallel execution and reporting, and leverage LLMs and AI agents (GitHub Copilot, Claude, OpenAI Assistants) for test generation and code review.

Responsibilities

• Design, enhance, and maintain scalable Java test automation frameworks
• Build end-to-end automation solutions using Selenium
• Guide framework architecture decisions with focus on scalability, maintainability, and reusability
• Implement parallel execution strategies and tune execution times across browsers and platforms
• Develop reusable libraries, utilities, and framework extensions
• Apply OOP principles including classes and objects, inheritance, polymorphism, encapsulation, and abstraction
• Design and implement REST and SOAP API automation solutions
• Validate XML and JSON payloads using serialization and deserialization techniques
• Implement authentication and authorization mechanisms such as Basic Authentication, Bearer Tokens, OAuth, and NTLM
• Handle session management and coordinate API integration testing
• Perform code reviews and reinforce clean coding standards
• Establish and govern coding standards, branching strategies, and version control best practices
• Integrate automation suites with Jenkins and GitHub Actions in CI/CD pipelines
• Design and implement AI-driven solutions for automated test case generation, test script generation, and code reviews

Requirements

• Proven experience in test automation engineering, typically 9–14 years
• High level of expertise with Java, Selenium, and automation framework architecture
• Strong command of Object-Oriented Programming and design patterns like Singleton, Factory, and Decorator
• Working knowledge of Page Object Model and Page Factory approaches
• Hands-on capability in REST and SOAP API automation with XML and JSON validation
• Practical CI/CD and DevOps integration skills using Jenkins and GitHub Actions
• Experience configuring static code analysis, quality gates, and automation reporting dashboards
• Hands-on work with Large Language Models including GitHub Copilot, Claude, and Cursor
• Clear understanding of Prompt Engineering, Context Window Management, and Tokenization
• Familiarity with embeddings, vector databases, and semantic search implementations
• Exposure to AI agents and AI-assisted engineering using tools such as OpenAI Assistants

Nice to have

• Experience building 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