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