GreyOrange
SDET III
- Compensation
- Not disclosed
- Location
- Gurgaon
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 1+ years
- Category
- Qa
- Posted
Skills
Must have
- Experience: 6-10 years in Software Engineering/SDET roles, with at least 1-2 years specializing in AI/ML-based automation.
- Coding Mastery: Deep proficiency in at least one object-oriented language and experience with "Pipeline as Code. "
- Problem Solving: Ability to debug complex distributed systems and microservices architectures.
- Education: B. S. / M. S. in Computer Science, Data Science, or a related technical field.
Full description
As an SDET III, you will be the bridge between development and quality, leveraging cutting-edge Generative AI and Autonomous Testing tools to accelerate our release cycles. You won't just be "testing" code; you will be building intelligent systems that monitor, diagnose, and repair the testing lifecycle.
Responsibilities:
- AI-Driven Framework Development: Design and maintain scalable automation frameworks (Java/Python) integrated with AI agents for self-healing scripts and autonomous test generation.
- Prompt Engineering for QA: Utilize LLMs (GitHub Copilot, Gemini) to generate complex test data, synthetic datasets, and boilerplate automation code.
- Creating network simulations (E2E Integration tests): Develop strategies for validating E2E integrations. A network simulator that can accommodate different datasets for multiple customers and predict if all integrations work as expected.
- Shift-Left and Observability: Integrate testing into CI/CD pipelines (Github Actions/GitLab/Jenkins) and utilize production observability (logs/metrics) to inform test design.
- Agentic Workflows: Orchestrate multi-agent AI systems that can autonomously explore the UI, discover edge cases, and report bugs without human intervention.
Requirements:
- Experience: 6-10 years in Software Engineering/SDET roles, with at least 1-2 years specializing in AI/ML-based automation.
- Coding Mastery: Deep proficiency in at least one object-oriented language and experience with "Pipeline as Code. "
- Problem Solving: Ability to debug complex distributed systems and microservices architectures.
- Education: B. S. / M. S. in Computer Science, Data Science, or a related technical field.
Soft Skills:
- Strategic Thinking: Moving beyond "Pass/Fail" to evaluate product "Quality of Experience. "
- Adaptability: A "learn-it-all" mindset to keep up with the monthly evolution of AI tooling.
- Communication: Ability to explain technical risks of AI outputs to non-technical stakeholders.
Technical Toolkit (2026 Standards):
- Languages: Java, Python, JavaScript, or C#.
- DevOps and CI/CD: Docker, Kubernetes, GitHub Actions, GitLab CI AI.
- Assistants: GitHub Copilot, Google Duet, or custom RAG-based QA bots.
- Frameworks: Selenium, Playwright, Cypress, Appium (Mobile), REST-assured, TestNG.
- Cloud Platforms: GCP, AWS, Azure.