Role Overview

We are seeking a Senior SDET / Automation QA Engineer with strong ownership of quality, deep expertise in backend/API automation, and hands-on experience in mobile automation. The ideal candidate is automation-first, comfortable working with production systems, and actively uses AI tools to improve test effectiveness and productivity.

- Strong debugging and problem-solving skills

- High ownership and accountability for quality

- Thinks beyond test cases and focuses on system reliability and scalability

- Comfortable working in fast-paced, evolving product environments

Key Responsibilities
- Design, develop, and maintain automation frameworks for backend/API and mobile applications

- Own API automation using REST Assured and/or Karate DSL

- Perform backend testing for microservices including business logic, integrations, and edge cases

- Build and maintain mobile automation suites using Appium (Android/iOS)

- Validate data using MySQL / SQL queries

- Analyze application and system logs to debug test and production failures

- Work in Linux/Unix environments for log analysis and troubleshooting

- Use Kibana / ELK dashboards to investigate issues and understand system behavior

- Leverage AI-powered tools (e.g., ChatGPT, Copilot, test generation tools) for automation, debugging,



and test optimization

- Integrate automation suites into CI/CD pipelines

- Collaborate closely with developers, product managers, and DevOps teams to shift quality left

- Drive automation best practices, test strategies, and quality metrics

Must-Have Skills
- 4–6 years of experience as SDET / Automation QA

- Solid experience in Backend & API Testing

- Hands-on API automation using REST Assured and/or Karate DSL

- Strong programming fundamentals (Java preferred)

- Experience with mobile automation using Appium

- Proficiency in MySQL / SQL for backend data validation

- Strong working knowledge of Linux/Unix

- Experience analyzing logs for debugging and root cause analysis

- Hands-on experience with Kibana / ELK stack

- Practical usage of AI tools to improve test coverage and efficiency

- Solid understanding of SDLC, testing principles, and automation best practices

Good to Have
- Experience testing microservices-based architectures

- Exposure to Kafka, RabbitMQ, or other message queues

- Performance testing exposure (JMeter or similar)

- CI/CD tools (Jenkins, GitHub Actions, GitLab CI)

Prior experience in a product-based company

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