Senior Software Quality Engineer - AI Assurance
Summary
Senior QA engineer at Sysvine Technologies in Chennai building AI-powered test automation and 'AI assurance' practices: turning specs into automated test suites across web, mobile, API, and AI platforms, evaluating/red-teaming LLMs and agents, and integrating quality gates into CI/CD.
Hot Duties:
Build next-generation products using AI-powered testing, intelligent automation, and continuous AI assurance. Apply Specs-Driven Testing (SDT) and Automation-First principles to turn requirements into verifiable specs, automated suites, and measurable outcomes across Web, Mobile, API, and AI platforms. Leverage AI agents, LLMs, MCP, and self-healing automation to expand coverage and boost release velocity. Conduct continuous evaluation, red teaming, and AI governance to ensure autonomous workflows stay reliable, secure, and aligned with business intent.
Duties:
- Build scalable frameworks across Web, Mobile, API, and AI platforms using SDT(optional), Automation-First, and Shift-Left strategies.
- Apply AI coding tools, LLMs, and agents for automated test generation, self-healing, data creation, and defect analysis.
- Integrate automated quality gates into CI/CD pipelines to convert business specs into executable validation suites.
- Execute API, performance, accessibility, security, and E2E testing aligned with technical and business risks.
- Drive AI Assurance and governance by red-teaming and evaluating LLMs, RAG, and autonomous agents for accuracy, bias, and safety.
- Set quality metrics, manage release readiness, and mentor teams on AI-assisted testing and modern QE practices.
Requirements:
- Hands-on proficiency in Java, Python, or JavaScript/TypeScript
- Experience with JUnit, TestNG, or equivalent testing frameworks and CI/CD platforms such as Jenkins or GitLab CI/CD
- Mastery of AI coding tools (Claude, Cursor, GitHub Copilot) to implement LLMs, AI agents, RAG, and prompt engineering in test workflows
- Strong application of Specs-Driven Testing (SDT), Shift-Left, and risk-based strategies to transform requirements into executable specs and verifiable outcomes
- Sharp analytical, problem-solving, and stakeholder management skills focused on end-to-end quality ownership
- AI output validation (accuracy, bias, security, safety) using MCP, AI evaluation frameworks, and AI observability
- Optional: API testing (Postman/REST Assured), performance (JMeter), accessibility (axe/Lighthouse), and mobile automation