Sr QA Automation Engineer - AI First
New
J
JobgetherAI Infrastructure
Brazil, North American Central TimeFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Fluent English
- Experience
- 5+ years
- Required Skills
- PythonKafkaKubernetesTypeScriptAPI testingCI/CDGitHub ActionsAzure DevOpsPlaywright
Requirements
- 5+ years of hands-on experience leading test automation initiatives for distributed systems and complex software platforms.
- Strong production experience with Playwright, including fixtures, parallel execution, trace analysis, and CI integration.
- Advanced proficiency in Python and TypeScript for automation development.
- Experience with API, contract, and integration testing, including mocking and service virtualization.
- Knowledge of event-driven architectures and messaging systems such as Kafka.
- Experience implementing CI/CD quality gates using platforms such as GitHub Actions or Azure DevOps.
- Familiarity with containerized environments and basic Kubernetes concepts.
- Experience applying AI-assisted testing approaches and evaluating AI-generated code or test scenarios critically.
- Strong debugging skills with a focus on root-cause analysis and long-term solutions.
- Fluent English communication skills for daily collaboration, documentation, and technical discussions.
- Ability to work independently while collaborating effectively with distributed teams across time zones.
Responsibilities
- Design, implement, and maintain comprehensive automated testing strategies covering unit, integration, contract, end-to-end, and edge deployment validation.
- Build and maintain UI automation suites using Playwright for operational interfaces and human-in-the-loop workflows.
- Develop API, integration, and contract testing solutions for Python microservices and event-driven systems, including Kafka-based pipelines.
- Integrate automated testing into CI/CD pipelines as mandatory quality gates with measurable standards.
- Monitor test performance, identify root causes of failures, reduce test instability, and improve automation reliability.
- Create evaluation frameworks for AI-driven workflows, including validation of tool selection, execution logic, escalation behavior, and system limitations.
- Establish regression testing strategies for non-deterministic AI components using evaluation datasets, tolerance criteria, and drift monitoring.
- Automate validation for edge environments, including offline scenarios, reconnection flows, and security boundaries.
- Collaborate with technical teams to transform requirements and operational processes into clear acceptance criteria.
- Mentor engineers on quality ownership, automation practices, and effective testing methodologies.
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