We are seeking an SDET who is passionate about test automation, quality engineering, and leveraging Generative AI to drive smarter, faster, and more efficient testing outcomes. The ideal candidate will have a strong foundation in automation frameworks, API and UI validation, and a forward-thinking mindset to integrate AI tools and techniques into everyday quality processes. This role is ideal for someone who enjoys building automation systems, optimizing pipelines, and experimenting with AI-driven testing strategies to scale quality with intelligence.
Responsibilities:
Design, develop, and maintain end-to-end automation frameworks for UI, API, and backend testing.
Leverage Generative AI tools to accelerate test case design, script generation, and data preparation.
Integrate AI-based validation and anomaly detection into automation workflows for smarter quality insights.
Collaborate with developers and product teams to ensure testability, performance, and reliability from the start.
Lead initiatives to reduce manual effort using intelligent automation and AI-driven utilities.
Drive best practices in shift-left testing, code quality, and continuous testing in agile sprints.
Mentor junior engineers on automation, coding standards, and the use of AI tools in testing.
The core requirements for the job include the following:
Automation Frameworks:
Expertise in Selenium / Playwright.
Strong programming skills in Java / Python / JavaScript.
Experience in building custom automation frameworks (hybrid, keyword, BDD – Cucumber / SpecFlow).
Familiarity with API testing using Postman, REST Assured, SuperTest, or equivalent.
Knowledge of test data management, mocking (WireMock), and contract testing (Pact).
AI and Smart Testing Tools:
Exposure to AI-assisted testing tools (e. g., Testim.io, Mabl, LambdaTest SmartTest, or similar).
Hands-on experience or interest in using GenAI tools (ChatGPT, Copilot, etc. ) for generating test data, test cases, and automating repetitive QA tasks.
Understanding of AI model testing, prompt validation, or data-driven AI validation is a plus.
DevOps and Cloud Integration:
Experience with CI/CD automation, Jenkins, GitHub Actions, or Azure DevOps.
Knowledge of Docker / Kubernetes and cloud environments (AWS / Azure).
Exposure to test environment orchestration and parallel execution on cloud platforms.
Performance and Reliability:
Familiarity with JMeter / k6 / Gatling for performance and load testing.
Basic awareness of observability tools (Grafana, ELK, Prometheus) for system quality insights.
Soft Skills:
Strong analytical, debugging, and problem-solving abilities.
Ability to think creatively about automation efficiency using AI.
Collaborative team player with excellent communication skills.
Curious mindset, open to experimenting with AI-driven quality engineering tools.
Preferred Qualifications:
Bachelor’s or Master’s in Computer Science, Engineering, or related field.
Proven experience in scaling automation across microservices and cloud-native applications.
Exposure to LLM-based productivity tools or building internal AI-driven test accelerators.
Experience with test analytics dashboards or quality intelligence systems.
