Sr. Software Test Automation Engineer – Playwright with TypeScript and Agentic AI

About the Role As a Senior Software Test Automation Engineer, you will design, develop, and evolve modern test automation solutions that improve software quality, engineering velocity, and test coverage. This is a highly hands-on engineering role focused on Playwright, TypeScript, API automation, CI/CD, and AI-enabled quality engineering. You will work closely with software engineers, product teams, and QE leaders to build scalable, maintainable automation and embed quality throughout the software development lifecycle. You will also explore and apply Agentic AI, LLM-based testing, AI-assisted test generation, self-healing automation, and intelligent test analysis to modernize traditional quality engineering practices. The successful candidate is a strong software engineer who understands testing deeply and can translate emerging AI capabilities into practical, reliable QE solutions. Key Responsibilities · Design, develop, and maintain scalable UI and API automation frameworks using Playwright and TypeScript. · Develop robust automated tests covering functional, regression, integration, API, and end-to-end scenarios. · Define and implement automation architecture, coding standards, reusable components, and testing patterns. · Partner with developers and product teams to establish testability, acceptance criteria, and automation strategy early in the development lifecycle. · Integrate automated tests into CI/CD pipelines and enable reliable, repeatable execution. · Analyze automation failures, identify root causes, and improve test reliability and maintainability. · Reduce flaky tests through effective synchronization, resilient locators, test isolation, test data management, and failure diagnostics. · Develop reusable automation utilities, libraries, skills, and tools that can be leveraged across teams and applications. · Apply Agentic AI and LLM capabilities to areas such as automated test generation, test case optimization and coverage analysis, AI-assisted test maintenance, self-healing or resilient automation, failure analysis and root-cause identification, test data generation, requirements-to-test traceability, intelligent regression selection, and LLM/AI application testing and evaluation. · Explore and implement AI agents, MCP-based integrations, and AI-assisted QE workflows where they provide measurable value. · Develop or contribute to evaluation strategies for AI-powered applications, including LLM response validation, hallucination detection, prompt evaluation, rubrics, golden datasets, and quality metrics. · Participate in architecture and code reviews and provide technical guidance on automation best practices. · Troubleshoot complex automation, environment, test-data, and integration issues. · Contribute to performance, reliability, and scalability testing as needed. · Monitor automation effectiveness using metrics such as coverage, execution time, stability, defect detection, and maintenance effort. · Mentor other engineers and promote modern automation and quality engineering practices. · Continuously evaluate emerging testing technologies and AI capabilities and identify opportunities to improve QE productivity and quality. Required Qualifications · Bachelor's degree in computer science, Engineering, or a related technical field, or equivalent experience. · 5+ years of software test automation or quality engineering experience, with strong hands-on engineering experience. · Strong hands-on experience with Playwright. · Strong programming experience with TypeScript and/or JavaScript. · Strong understanding of UI, API, integration, and end-to-end automation. · Experience designing or contributing to test automation frameworks and architecture. · Strong understanding of software development practices, Git, code reviews, debugging, and CI/CD. · Experience integrating automated testing into CI/CD pipelines such as GitHub Actions, Jenkins, Azure DevOps, or GitLab. · Experience with API testing and tools/frameworks such as REST, Postman, Playwright API, or equivalent. · Strong understanding of test design techniques, test strategy, test coverage, and risk-based testing. · Demonstrated ability to troubleshoot complex automation failures and identify root causes. · Experience working in Agile/Scrum software development environments. · Strong communication and collaboration skills. Agentic AI / AI Testing Experience · Generative AI / LLM application testing · AI-assisted software development or test automation · AI agents / Agentic AI workflows · MCP or similar agent-to-tool integration frameworks · AI-generated test cases or test scripts · LLM evaluation and testing · Prompt engineering and prompt evaluation · RAG testing and evaluation · Hallucination and factuality testing · Golden datasets and evaluation datasets · Rubric-based evaluation · AI-powered test maintenance or self-healing automation · AI-based failure analysis or test-result summarization · Experience using tools such as GitHub Copilot, Claude, ChatGPT, or other AI coding/testing assistants Required Qualifications · Experience with Playwright using TypeScript at enterprise scale. · Experience migrating automation from Selenium to Playwright. · Experience with BDD/Gherkin and translating requirements or acceptance criteria into automated tests. · Experience with API automation and contract testing. · Experience with performance/load testing tools such as JMeter, k6, or equivalent. · Experience with Docker, Kubernetes, or cloud-based test environments. · Experience with AWS, Azure, or other cloud platforms. · Experience with test management and defect tools such as Jira, Azure DevOps, qTest, or equivalent. · Experience building reusable internal automation libraries, tools, AI agents, or developer/QE skills. · Experience with RAG, embeddings, vector databases, or LLM application architectures. · Experience establishing quality metrics and automation effectiveness measures. What Success Looks Like · Build production-quality Playwright/TypeScript automation, not simply execute or maintain existing scripts. · Increase automation coverage while reducing manual regression effort. · Build automation that is stable, maintainable, scalable, and CI/CD ready. · Reduce flaky-test rates and improve failure diagnostics. · Identify opportunities where AI/Agentic AI can eliminate repetitive QE work. · Create reusable automation components and AI-enabled capabilities that can be adopted by other QE teams. · Collaborate directly with developers to identify and prevent quality issues earlier in the SDLC. · Demonstrate measurable improvements in automation coverage, execution speed, reliability, defect detection, and QE productivity.