Software Engineer in Test

6 days ago

Bengaluru, Karnataka, India Landmark Group Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Experience range: 5 to 12 years

Key Responsibilities


• Test Automation Development: Build and maintain robust, scalable test automation frameworks for API, web, and mobile platforms.
• Design, develop, and maintain internal tools that enhance automation capabilities, CI/CD integration, and test data visualization.
• Build intelligent dashboards for real-time reporting of test results, release quality metrics, and defect analytics.
• Develop AI/ML-based solutions to optimize test case prioritization, test suite reduction, flakiness prediction, and self-healing scripts.
• Integrate tools and dashboards with CI pipelines (e.g., Jenkins, GitHub Actions) and test frameworks (Selenium, Playwright, etc.).
• Work closely withSREs, and DevOps teams to ensure tools are scalable, performant, and well-adopted.
• Create APIs, microservices, or CLI tools that support testing workflows, artifact management, and defect traceability.
• Continuously evaluate new AI/ML advancements and incorporate them into test engineering practices.

Required Skills


• Proficiency in Java, Python, , or similar scripting languages.
• Hands-on experience with dashboarding tools (Grafana, Kibana, Superset, or custom solutions using ).
• Working knowledge or experience with Mabl, TestRigor, or any AI automation tool.
• Strong understanding of test automation frameworks (e.g., Pytest, Playwright, Cypress, Selenium).
• Experience integrating tools with CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI).
• Familiarity with databases (SQL, NoSQL) and working with test data at scale.
• Experience in building REST APIs or microservices for internal use.

Nice to Have


• Experience with GenAI or LLM integration for test case generation or failure analysis.
• Working knowledge of AI/ML libraries like Scikit-learn, TensorFlow, or transformers for testing-centric use cases.
• Exposure to containerization (Docker/K8s) and cloud platforms (AWS/GCP/Azure).
• Understanding of observability tools like Prometheus or ELK stack.
• Knowledge of software telemetry, test coverage reporting, and static/dynamic code analysis tools.

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