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