As a 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.

Key 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: Utilise 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 & Observability: Integrate testing into CI/CD pipelines (Github Actions/GitLab/Jenkins) and utilise 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.
- Technical Toolkit (2026 Standards)

Languages: Java, Python, JavaScript, or C#

DevOps & 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

Required Qualifications

- Experience: 6-9 years in Software Engineering/SDET roles, with at least 1–2 years specialising 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.