Senior Java Automation Test Engineer with AI

Senior Java Automation Test Engineer

Responsibilities

• Design, assemble, enhance, and sustain scalable test automation frameworks in Java
• Build stable Selenium-based automation solutions for UI testing
• Drive framework architectural decisions to maximize reuse, maintainability, and scalability
• Optimize parallel execution and shorten run times across multiple browsers and platforms
• Deliver reusable utilities, shared libraries, and framework add-ons
• Engineer API automation solutions that cover REST and SOAP services
• Verify XML and JSON payloads using serialization and deserialization techniques
• Implement authentication and authorization schemes such as Basic Authentication, Bearer Tokens, OAuth, and NTLM
• Coordinate session management and run API integration testing
• Review code and reinforce clean coding discipline across the team
• Establish and uphold standards for coding, branching approaches, and version control usage
• Wire automation suites into CI/CD pipelines through Jenkins and GitHub Actions
• Create AI-driven capabilities for test case generation, script generation, code review assistance, and API-based automation workflows

Requirements

• 6–9 years of practical experience delivering test automation
• Excellent Java skills with strong Object-Oriented Programming fundamentals: classes, objects, inheritance, polymorphism, encapsulation, and abstraction
• Advanced experience creating scalable test automation frameworks and working with Selenium
• Hands-on expertise automating REST and SOAP API tests, including XML and JSON validation
• Solid understanding of design patterns including Singleton, Factory, and Decorator
• Working knowledge of Page Object Model and Page Factory practices
• Experience integrating CI/CD and DevOps workflows using Jenkins and GitHub Actions, plus static code analysis and quality gate setup
• Practical exposure to Large Language Models including GitHub Copilot, Claude, and Cursor
• Clear understanding of prompt engineering, context window management, and tokenization
• Familiarity with model selection techniques, embeddings, and vector databases for semantic search use cases
• Proven ability to design and build AI agents and AI-assisted engineering solutions using OpenAI Assistants, GitHub Copilot, and Claude
• Knowledge of similarity search approaches and retrieval mechanisms

Nice to have

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