Software Engineer in Test

23 hours ago

bangalore district, India Landmark Group Full time

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, Node.js, or similar scripting languages. • Hands-on experience with dashboarding tools (Grafana, Kibana, Superset, or custom solutions using React/D3.js). • 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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