Primary responsibilities
Test strategy and technical leadership
Define and own the test strategy, framework architecture, and tooling roadmap for assigned product areas.
Translate business requirements into traceable test cases across functional, regression, integration, and end-to-end dimensions; lead test plan reviews with developers, product owners, and project managers.
Define test coverage metrics and quality gates; produce risk-based release readiness reports for engineering leadership at each milestone.
Test automation and AI-assisted engineering
AI-assisted development: Leverage GitHub Copilot, Claude Code, and comparable AI coding assistants to accelerate test script authorship, generate edge-case scenarios, and perform intelligent code review.
AI agentic testing: Configure and evaluate agentic workflows for autonomous test generation and self-healing locators; apply Model Context Protocol (MCP) to connect AI agents to test tools, APIs, and data sources for end-to-end agentic test workflows; define system instructions, skills, and tool configurations for AI coding agents.
Architect and maintain scalable automation frameworks using Selenium, Playwright, or Cypress for UI; REST Assured or similar for APIs; integrate into CI/CD pipelines via Azure DevOps or GitHub Actions.
Write complex SQL queries to validate backend data integrity in PostgreSQL (using DBeaver); build data-driven test suites for parallel and cross-browser execution.
Defect management, collaboration, and mentorship
Isolate, document, and track defects in Azure DevOps; lead exploratory testing sessions and root-cause analysis on escaped defects.
Serve as quality engineering lead across Agile delivery teams; participate in sprint planning, backlog grooming, standups, and retrospectives.
Conduct code reviews and coach developers and team members on shift-left testing practices and quality engineering standards.
Required qualifications
Core
B.E./B.Tech. in Computer Science, MIS, or related discipline; 4 to 7 years in software quality engineering with the majority in test automation and framework development on a commercial product.
Production-quality automation code in C#, Java, Python, or JavaScript/TypeScript; proficiency with Selenium, Playwright, or Cypress; API testing with REST Assured, Postman, or SoapUI.
CI/CD pipeline experience (Azure DevOps, GitHub Actions, or Jenkins); strong Git skills; cross-browser testing with BrowserStack or equivalent; solid SQL skills for database validation.
Deep understanding of QA lifecycle and Agile/Iterative delivery; clear communicator able to produce test strategies and quality summaries for both technical and non-technical audiences.
Demonstrated experience with performance engineering, scalability design, and observability practices (logging, metrics, distributed tracing) in production systems.
Hands-on experience with AWS services including Lambda, ECS, RDS, API Gateway, CloudWatch, and related cloud-native tooling.
AI and agentic development tools
Practical daily use of AI coding assistants (GitHub Copilot, Claude Code, or Cursor) to write, refactor, and review test code; proven ability to craft effective prompts and system instructions.
Solid understanding of AI agents, skills, and tool-calling; familiarity with Model Context Protocol (MCP) — ability to connect AI models to internal tools, APIs, and test infrastructure for multi-step agentic workflows; working knowledge of an LLM API or orchestration framework (Anthropic API, LangChain, or similar) is a strong plus.
Preferred qualifications
Performance and/or security testing (JMeter, k6, OWASP ZAP); TDD/BDD methodologies (Cucumber, Reqnroll); containerized test environments (Docker, Kubernetes).
Working knowledge of JavaScript/TypeScript; familiarity with Visual Studio and the Microsoft development ecosystem; ISTQB certification or equivalent credential.
Knowledge of AWS services (SNS, S3, EC2, Lambda, etc.) is an advantage; familiarity with cloud-based test environment provisioning and infrastructure as code.
