The application window is expected to close on: 09/28/2026
Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received.
Your ImpactAs an
Automation QA Engineer, you will drive the transformation of our regression testing ecosystem, moving beyond manual processes to build AI-driven, fully automated test suites for both new and legacy features. You will design and own end-to-end (E2E), API, and performance test automation that ensures the Circuit platform continues to meet Cisco's high standards for functional quality and performance.
You will build and maintain scalable test frameworks, integrate them into CI/CD pipelines for continuous quality feedback, and leverage AI-assisted coding tools (such as Cursor, Copilot, or Claude Code) to generate and maintain tests-while rigorously validating that AI-generated artifacts are stable, correct, and compliant with security standards. This is a high-visibility role where your ability to automate functional quality and reduce technical debt directly ensures Circuit remains a reliable vehicle for process automation across Cisco.
ResponsibilitiesAutomated Regression & E2E Testing: Architect, build, and maintain comprehensive automated regression and end-to-end test suites for the entire Circuit platform using
Playwright (TypeScript/JavaScript),
Python/Pytest, and Selenium, replacing manual QA with scalable automation.
API Testing: Design and automate
REST and
GraphQL API test suites covering functional correctness, contract validation, error handling, and performance-including validation of AI/model-serving endpoints under concurrent load.
Performance & Load Testing: Plan and execute load, stress, and soak tests using
k6 or JMeter, ; track response-time percentiles (p95/p99), throughput, error rates, and AI-specific metrics such as Time to First Token (TTFT) and inter-token latency.
AI-Driven Test Automation: Use AI agents and AI-assisted coding tools (Cursor, Copilot, Claude Code) to generate and maintain regression suites for new and legacy features; read and judge the stability of auto-generated code and ensure it does not violate security standards before it is merged.
AI Application & Agent Validation: Develop testing approaches for AI/LLM features-validating model response quality, behavior consistency (stability testing), and end-to-end AI agent workflows across both the reasoning and action layers.
CI/CD Integration & Reporting: Integrate all automated tests into
CI/CD pipelines (GitHub Actions, Jenkins, or equivalent) for continuous validation; build dashboards and reports (Grafana, Allure) and communicate quality metrics, risk assessments, and defect trends to engineering leadership.
Cross-Functional Leadership: Act as a "Team Captain" for automation initiatives-defining standards, mentoring peers, and collaborating with development, product, and UX teams to embed automation-first testing into the core development lifecycle.
Minimum Qualifications- Bachelor's degree with 4+ years, Master's degree with 3+ years, or PhD with 1+ year of related experience in Computer Science, AI/ML, or a related technical field.
- Experience in test automation and regression framework development, with a deep understanding of functional quality assurance.
- Experience with Playwright (TypeScript/JavaScript) for E2E/UI automation and/or Python with Pytest/Robot Framework; familiarity with Selenium.
- Experience with REST API testing (and familiarity with API contract testing); GraphQL testing a plus.
- Solid conceptual and practical knowledge of software development lifecycles, including CI/CD pipelines (GitHub Actions, Jenkins, or similar) and automated testing best practices.
- Experience in AI-assisted coding tools (e.g., GitHub Copilot, Cursor, Claude Code), critical thinking, and Agile/Git workflows to ensure high-quality, scalable test automation
Preferred Qualifications- Experience testing AI/ML-powered web applications, including LLM-based features, chatbots, or AI agent workflows.
- Hands-on experience with performance/load testing (k6, JMeter) and AI/LLM performance metrics (TTFT, inter-token latency, token throughput).
- Experience in AI Ops / MLOps, or building/fine-tuning custom LLMs.
- Experience with observability and reporting tools such as Splunk, Grafana, Prometheus,
- Familiarity with Docker and Kubernetes for test environment management, and with Google Cloud Platform (GCP).
- Proven track record of leading technical projects and mentoring junior engineers.
- Strong communication and collaboration skills; ability to influence cross-functional stakeholders.
- Experience building and productizing agentic applications with a focus on evaluation and business acceptance.
