Senior Java Automation Test Engineer with AI

Senior Java Automation Test Engineer

Responsibilities

• Plan, implement, evolve, and maintain scalable Java automation frameworks
• Create dependable UI automation leveraging Selenium
• Lead architecture decisions for frameworks with emphasis on reuse, scalability, and maintainability
• Apply parallel execution strategies and improve execution times across browsers and platforms
• Build shared utilities, reusable libraries, and framework extensions
• Develop API automation solutions that support REST and SOAP services
• Validate XML and JSON payloads through serialization and deserialization methods
• Configure authentication and authorization patterns including Basic Authentication, Bearer Tokens, OAuth, and NTLM
• Supervise session management and conduct API integration testing
• Conduct code reviews and ensure clean coding discipline is followed
• Define and enforce coding standards, branching approaches, and version control practices
• Integrate automation suites with CI/CD pipelines using Jenkins and GitHub Actions
• Build AI-driven capabilities for automated test case creation, script generation, code review support, and API-based automation workflows

Requirements

• 6–9 years of hands-on test automation experience
• Strong Java proficiency with Object-Oriented Programming fundamentals: classes, objects, inheritance, polymorphism, encapsulation, and abstraction
• Deep experience designing scalable test automation frameworks and working with Selenium
• Demonstrated capability in REST and SOAP API automation, including XML and JSON payload validation
• Sound knowledge of software design patterns such as Singleton, Factory, and Decorator
• Experience applying Page Object Model and Page Factory approaches
• Experience integrating CI/CD and DevOps workflows using Jenkins and GitHub Actions, including static code analysis and quality gate implementation
• Hands-on experience with Large Language Models like GitHub Copilot, Claude, and Cursor
• Solid understanding of prompt engineering, context window management, and tokenization
• Working knowledge of model selection methods, embeddings, and vector databases for semantic search use cases
• Ability to design and build AI agents and AI-assisted engineering solutions using OpenAI Assistants, GitHub Copilot, and Claude
• Understanding of similarity search techniques and retrieval mechanisms

Nice to have

• Experience with agentic workflows and collaborative multi-agent systems for end-to-end testing, automated reporting, and workflow coordination
• Familiarity with Retrieval-Augmented Generation architectures and integrating LLMs with enterprise knowledge sources
• Understanding of Model Context Protocol concepts and related frameworks
• Knowledge of Attention Mechanisms and Transformer-based model design