Lead Javascript Automation Test Engineer with AI & Agentic Testing

We're seeking a Lead JavaScript Automation Test Engineer to join our team and take on a key role in guaranteeing the accuracy and reliability of our software systems. This position oversees a team of testers, manages and implements automated testing workflows, and supports ongoing enhancements to our test automation framework. We're looking for someone who can build scalable automation solutions, champion quality engineering standards, guide team members, and apply Generative AI and Agentic AI technologies to speed up software testing and delivery. Responsibilities Lead and mentor a team of testers, offering direction and support throughout testing activities Work with cross-functional teams to collect and evaluate system requirements Create detailed test plans, test cases, and testing strategies Organize and prioritize testing tasks to achieve efficient and comprehensive coverage Build, develop, and run automation scripts using advanced testing tools Report and track defects, ensuring they are properly validated and resolved Establish and uphold quality standards in line with best practices Build and implement API automation solutions for both REST and SOAP services Verify XML and JSON payloads using serialization and deserialization methods Connect automation suites to CI/CD pipelines through Jenkins and GitHub Actions Lead in-sprint automation efforts, shift-left testing approaches, and Test Pyramid strategies Guide team members on automation frameworks, design patterns, and quality engineering techniques Requirements 9 to 14 years of experience in test automation and quality engineering Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline Strong knowledge of authentication and authorization methods, including Basic Authentication, Bearer Tokens, OAuth, and NTLM Familiarity with software design patterns such as Singleton, Factory, Decorator, Page Object Model (POM), and Page Factory Experience setting coding standards, branching strategies, and version control best practices Ability to configure static code analysis, quality gates, and automation reporting dashboards Understanding of Definition of Ready (DoR), Definition of Done (DoD), Confirmation Testing, Regression Testing, and Change Impact Testing Practical experience with LLMs including GitHub Copilot, Claude, and Cursor Knowledge of Prompt Engineering, Context Window Management, Tokenization, and Model Selection Strategies Experience creating and applying embeddings for semantic search use cases, such as similarity search and retrieval Skill in designing AI-powered solutions for automated test case and script generation, code reviews, linting checks, and API-based automation workflows using tools like Claude, GitHub Copilot, Cursor, and OpenAI Assistants Nice to have Experience developing multi-agent workflows and collaborative agent systems for End-to-End Testing, Automated Reporting, Failure Recovery, Fallback Handling, and Workflow Orchestration Familiarity with RAG architectures and integrating LLMs with enterprise knowledge sources Knowledge of Model Context Protocol concepts and frameworks, including orchestrating agent interactions, task delegation, and workflow monitoring Awareness of Attention Mechanisms and Transformer-based model architectures