Lead JavaScript Automation Test Engineer with AI & Agentic Testing

Lead JavaScript Automation Test Engineer

Responsibilities

• Lead and mentor a team of testers, providing guidance and support in testing activities
• Collaborate with cross-functional teams to gather and analyze system requirements
• Develop comprehensive test plans, test cases, and test strategies
• Coordinate and prioritize testing activities to ensure efficient and thorough testing coverage
• Design, develop, and execute automation scripts using advanced automation tools
• Identify, report, and track defects, and ensure their thorough validation and resolution
• Define and enforce quality standards and ensure adherence to best practices
• Design and implement API automation solutions for REST and SOAP services
• Validate XML and JSON payloads through serialization and deserialization techniques
• Integrate automation suites with CI/CD pipelines using Jenkins and GitHub Actions
• Drive in-sprint automation initiatives, shift-left testing practices, and Test Pyramid principles
• Mentor team members on automation frameworks, design patterns, and quality engineering practices

Requirements

• 9 to 14 years of experience in software test automation and quality engineering
• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field
• Expertise in authentication and authorization mechanisms including Basic Authentication, Bearer Tokens, OAuth, and NTLM
• Knowledge of software design patterns including Singleton, Factory, Decorator, Page Object Model (POM), and Page Factory
• Skills in establishing coding standards, branching strategies, and version control best practices
• Proficiency in configuring static code analysis, quality gates, and publishing automation reports and dashboards
• Familiarity with Definition of Ready (DoR), Definition of Done (DoD), Confirmation Testing, Regression Testing, and Change Impact Testing
• Hands-on experience with LLMs such as GitHub Copilot, Claude, and Cursor
• Understanding of Prompt Engineering, Context Window Management, Tokenization, and Model Selection Strategies
• Experience generating and utilizing embeddings for semantic search applications, including similarity search and retrieval mechanisms
• Capability to design and implement AI-driven solutions for automated test case generation, test script generation, code reviews, linting validation, and API-driven automation workflows using tools such as Claude, GitHub Copilot, Cursor, and OpenAI Assistants

Nice to have

• Experience building multi-agent workflows and designing collaborative agent systems for End-to-End Testing, Automated Reporting, Failure Recovery and Fallback Handling, and Workflow Orchestration
• Knowledge of RAG architectures and experience integrating LLMs with enterprise knowledge sources
• Understanding of Model Context Protocol concepts and frameworks, with the ability to orchestrate agent interactions, task delegation, and workflow monitoring
• Understanding of Attention Mechanisms and Transformer-based model architectures