Tooling & AI Test Automation Engineer ( SDET)

LANDMARK GROUP

4 - 9 years

14 - 24 Lacs

Bengaluru

Posted:Just now| Platform:

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Skills Required

Automation Automation Testing Selenium ML Scripting Jenkins Java GitHub Ci/Cd Python Framework Development Playwright Automation

Work Mode

Work from Office

Job Type

Full Time

Job Description

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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