Principal QA Engineer

Svitla Systems Inc. is looking for a Principal QA Engineer for a full-time position (40 hours per week) in Europe. Our client is a comprehensive tax filing software company that offers a wide range of features.

As a Principal QA Engineer, you will define the testing angles (functional, end-to-end, API, contract, performance, and synthetics), decide how much to invest in each, and build them out using AI coding assistants. The role calls for someone with strong opinions on how testing should be done, who acts as the owner and driver of quality and leads the team toward a clear end state.

The right person is a self-directed QA leader who can take a loosely defined platform, set a clear test strategy, and build it out hands-on without being guided. They are fluent in modern API quality (public and internal APIs, contract testing, versioning, performance in CI/CD, and AI model and agent testing) and use Docker and AI coding assistants, such as Cursor or Claude, every day, by choice.

Requirements

• Daily use of AI coding assistants (Cursor, Claude, or similar) as part of regular work, and genuine enthusiasm for them.
• Concrete examples of using AI to generate, refactor, and maintain test code at scale.
• Ability to take a loosely defined area and own the test approach with minimal direction.
• Ability to explain a structured methodology clearly and unprompted, from strategy down to specific tests.
• Experience leading QA strategy for a product or platform, not only executing test cases.
• 8+ years in QA or test engineering, with recent experience at the senior, staff, or principal level.
• Experience in test strategy design: test pyramid, risk-based prioritization, and coverage definition.
• Experience with API testing at both public and internal levels, driven from OpenAPI specs (REST; gRPC is a plus).
• Experience with contract testing (such as Pact) and API versioning and backward-compatibility practices.
• Experience with performance and load testing using tools such as k6, Gatling, JMeter, or Locust, and knowledge of where these belong in a CD pipeline.
• Experience with testing approaches for ML models and AI agents (evaluations and non-deterministic output handling).
• Hands-on experience with Docker and containerized environments (such as Testcontainers and docker-compose test setups).
• Strong coding skills in TypeScript and experience with Playwright.
• Experience with CI/CD integration (GitHub Actions, GitLab CI, or similar).
• Fluent professional English.

Will be a plus

• Experience with synthetic monitoring tools (Checkly, Datadog Synthetics, Grafana Synthetic Monitoring, or similar).
• Observability experience: using metrics, logs, and traces to diagnose failures.
• Experience with LLM evaluation frameworks (such as promptfoo, DeepEval, and Ragas).
• Experience with Kubernetes and cloud platforms (AWS, GCP, or Azure).
• Experience mentoring developers on testing practices in a small team.
• Background as a software engineer before moving into QA.

Responsibilities

• Own the test strategy: start from a bird’s-eye view, map the platform’s test areas, identify critical paths, define coverage targets, and decide how much to invest in each testing layer.
• Test APIs at every level: design and build tests for both public APIs and internal service-to-service APIs, working directly from OpenAPI specifications.
• Protect backward compatibility: introduce contract testing (such as Pact or schema-based contracts) and define an API versioning and deprecation approach with the engineering team.
• Expand end-to-end coverage: grow the existing Playwright (TypeScript) suite well beyond happy paths, focusing on the most business-critical flows.
• Build synthetic monitoring: design and implement synthetics that continuously verify the cloud platform is up and catch service degradation early.
• Lead performance and load testing: define performance baselines, build load tests (such as k6, Gatling, or Locust), and decide where they run in the CI/CD pipeline.
• Test AI features: define practical approaches for testing models and AI agents, including non-deterministic outputs, evaluation criteria, and regression checks.
• Embed quality in CI/CD: place each test type at the right pipeline stage, keep runs fast and reliable, and make results visible to the team.
• Work AI-first: use AI coding assistants (Cursor, Claude, Copilot) daily to generate, maintain, and scale test suites, and coach developers to do the same.
• Drive and report: act as owner of quality, guide developers on their own testing practices, and send a weekly written update on progress, coverage, and risks.