Lead Javascript Automation Test Engineer with AI & Agentic Testing

Our team is seeking a Lead JavaScript Automation Test Engineer who will help guarantee the accuracy and dependability of our software systems. In this position, you will manage a team of testers, oversee automated testing operations, and support 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 tools to speed up testing and delivery cycles.

Responsibilities

• Lead and mentor a team of testers, offering direction and support throughout testing activities
• Partner 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, comprehensive coverage
• Build and run automation scripts using advanced automation tooling
• Track, document, and resolve defects, confirming thorough validation
• Establish and maintain quality standards aligned with best practices
• Build API automation solutions supporting REST and SOAP services
• Verify XML and JSON payloads via serialization and deserialization methods
• Connect automation suites to CI/CD pipelines through Jenkins and GitHub Actions
• Promote in-sprint automation, shift-left testing, and Test Pyramid principles
• Guide team members on automation frameworks, design patterns, and quality engineering approaches
  
  

Requirements

• 9 to 14 years working in software 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
• Ability to set coding standards, branching strategies, and version control best practices
• Experience configuring static code analysis, quality gates, and automation reporting dashboards
• Working knowledge of Definition of Ready (DoR), Definition of Done (DoD), Confirmation Testing, Regression Testing, and Change Impact Testing
• Direct experience using LLMs like GitHub Copilot, Claude, and Cursor
• Solid grasp 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
• Ability to build AI-powered solutions for automated test case and script generation, code reviews, linting checks, and API-driven automation workflows using tools like Claude, GitHub Copilot, Cursor, and OpenAI Assistants
  
  

Nice to have

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