{"id":3964778,"url":"https://alion.io/job/svitla-systems-principal-qa-engineer","title":"Principal QA Engineer","company":{"id":2216,"name":"Svitla Systems","domain":"svitla.com","url":"https://alion.io/company/svitla-systems","size_band":null,"is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":80,"open_postings":10,"ghost_share":0,"stale_share":0.8,"repost_share":0,"time_to_fill_p50_days":43,"computed_at":"2026-11-09T06:03:00Z"}},"role":"QA","role_family":"QA","seniority":"lead","employment_type":null,"work_mode":"remote","remote_scope":"unspecified","remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Cursor","optional":false},{"name":"Docker","optional":false},{"name":"gRPC","optional":false},{"name":"Rest API","optional":false},{"name":"AI Agents","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Copilot","optional":true},{"name":"Datadog","optional":true},{"name":"DeepEval","optional":true},{"name":"Gatling","optional":true},{"name":"GCP","optional":true},{"name":"GitHub Actions","optional":true},{"name":"GitLab CI","optional":true},{"name":"Grafana","optional":true},{"name":"Java","optional":true},{"name":"JMeter","optional":true},{"name":"k6","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true},{"name":"Locust","optional":true},{"name":"Pact","optional":true},{"name":"Playwright","optional":true},{"name":"Promptfoo","optional":true},{"name":"Ragas","optional":true},{"name":"Testcontainers","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-11-09T13:18:12Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-09T13:31:57Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"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.\nAs 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.\nThe 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.\nRequirements\nDaily use of AI coding assistants (Cursor, Claude, or similar) as part of regular work, and genuine enthusiasm for them.\nConcrete examples of using AI to generate, refactor, and maintain test code at scale.\nAbility to take a loosely defined area and own the test approach with minimal direction.\nAbility to explain a structured methodology clearly and unprompted, from strategy down to specific tests.\nExperience leading QA strategy for a product or platform, not only executing test cases.\n8+ years in QA or test engineering, with recent experience at the senior, staff, or principal level.\nExperience in test strategy design: test pyramid, risk-based prioritization, and coverage definition.\nExperience with API testing at both public and internal levels, driven from OpenAPI specs (REST; gRPC is a plus).\nExperience with contract testing (such as Pact) and API versioning and backward-compatibility practices.\nExperience with performance and load testing using tools such as k6, Gatling, JMeter, or Locust, and knowledge of where these belong in a CD pipeline.\nExperience with testing approaches for ML models and AI agents (evaluations and non-deterministic output handling).\nHands-on experience with Docker and containerized environments (such as Testcontainers and docker-compose test setups).\nStrong coding skills in TypeScript and experience with Playwright.\nExperience with CI/CD integration (GitHub Actions, GitLab CI, or similar).\nFluent professional English.\nWill be a plus\nExperience with synthetic monitoring tools (Checkly, Datadog Synthetics, Grafana Synthetic Monitoring, or similar).\nObservability experience: using metrics, logs, and traces to diagnose failures.\nExperience with LLM evaluation frameworks (such as promptfoo, DeepEval, and Ragas).\nExperience with Kubernetes and cloud platforms (AWS, GCP, or Azure).\nExperience mentoring developers on testing practices in a small team.\nBackground as a software engineer before moving into QA.\nResponsibilities\nOwn 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.\nTest APIs at every level: design and build tests for both public APIs and internal service-to-service APIs, working directly from OpenAPI specifications.\nProtect backward compatibility: introduce contract testing (such as Pact or schema-based contracts) and define an API versioning and deprecation approach with the engineering team.\nExpand end-to-end coverage: grow the existing Playwright (TypeScript) suite well beyond happy paths, focusing on the most business-critical flows.\nBuild synthetic monitoring: design and implement synthetics that continuously verify the cloud platform is up and catch service degradation early.\nLead 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.\nTest AI features: define practical approaches for testing models and AI agents, including non-deterministic outputs, evaluation criteria, and regression checks.\nEmbed 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.\nWork AI-first: use AI coding assistants (Cursor, Claude, Copilot) daily to generate, maintain, and scale test suites, and coach developers to do the same.\nDrive and report: act as owner of quality, guide developers on their own testing practices, and send a weekly written update on progress, coverage, and 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