Lead Javascript Automation Test Engineer with AI & Agentic Testing in Pune, भारत

We're seeking a Lead JavaScript Automation Test Engineer to join our team, taking on a key role in guaranteeing our software systems' accuracy and reliability. The position involves leading a team of testers, overseeing automated testing processes, and driving 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 testing and delivery cycles.

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 thorough test plans, test cases, and testing strategies Organize and prioritize testing tasks to achieve efficient and comprehensive coverage Build and run automation scripts through the use of advanced automation tools Track, document, and report defects, confirming their validation and resolution Establish and uphold quality benchmarks in line with best practices Build API automation frameworks 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 Champion in-sprint automation, shift-left testing approaches, and Test Pyramid principles Guide team members on automation frameworks, design patterns, and quality engineering fundamentals 9 to 14 years working in software test automation and quality engineering A Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a comparable discipline Strong background in authentication and authorization methods, covering 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 guidelines Experience configuring static code analysis, quality gates, and generating automation reports and dashboards Working knowledge of Definition of Ready (DoR), Definition of Done (DoD), Confirmation Testing, Regression Testing, and Change Impact Testing Practical experience using LLMs like GitHub Copilot, Claude, and Cursor Solid grasp of Prompt Engineering, Context Window Management, Tokenization, and Model Selection Strategies Background in creating and applying embeddings for semantic search, including similarity search and retrieval processes Skill in developing AI-powered solutions for automated test case and script generation, code reviews, linting checks, and API-based automation workflows using Claude, GitHub Copilot, Cursor, and OpenAI Assistants Background in creating multi-agent workflows and collaborative agent systems covering End-to-End Testing, Automated Reporting, Failure Recovery and Fallback Handling, and Workflow Orchestration Familiarity with RAG architectures and connecting LLMs to enterprise knowledge bases Grasp of Model Context Protocol concepts and frameworks, including the ability to coordinate agent interactions, delegate tasks, and monitor workflows Knowledge of Attention Mechanisms and Transformer-based model structures