{"id":3292105,"url":"https://alion.io/job/qualysoft-java-qa-automation-engineer-banking","title":"Java QA Automation Engineer","company":{"id":6361,"name":"Qualysoft","domain":"qualysoft.com","url":"https://alion.io/company/qualysoft","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":74,"open_postings":68,"ghost_share":0.515,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":21,"computed_at":"2026-10-31T05:58:55Z"}},"role":"QA","role_family":"QA","seniority":"middle","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bucharest, Romania"],"countries":["RO"],"hiring_countries":["RO"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Cypress","optional":false},{"name":"Function Calling","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Jackson","optional":false},{"name":"Java","optional":false},{"name":"Jenkins","optional":false},{"name":"Langfuse","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"MSW","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Playwright","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Promptfoo","optional":false},{"name":"Rest API","optional":false},{"name":"Selenium","optional":false},{"name":"SQL","optional":false},{"name":"Structured 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output reliability, detect drift, and gate releases on quality/stability thresholds.\nImprove test maintainability, remove flakiness, and optimize runtime.\nLLM QA Tooling\nUse LangChain4j for JVM-native LLM application support, including agents, RAG, and guardrails.\nUse Langfuse Java SDK and OpenTelemetry for trace capture, prompt/version tracking, and experiment comparison.\nUse the OpenAI Java SDK for structured outputs, function calling, webhook verification, and response validation.\nUse Promptfoo in CI when you need language-agnostic regression and red-team checks around the Java stack.\nUse Java schema validation and guardrail patterns with Jackson, Swagger annotations, and LangChain4j guardrails.\nUse OpenTelemetry-based traces and Langfuse datasets as the reproducible source of truth for release gates.\nQualifications:\n3+ years in QA automation for production systems.\nStrong Java backend testing skills.\nStrong frontend testing.\nExperience building and maintaining Playwright/Cypress/Selenium E2E suites.\nAPI testing experience (REST, schema validation, auth/error scenarios).\nExperience with SQL/PostgreSQL testing and test data management.\nCI/CD pipeline experience (GitHub Actions, Azure Pipelines, or Jenkins).\nPractical understanding of testing non-deterministic LLM outputs.\nNice to have:\nFamiliarity with Java-compatible LLM evaluation and regression tooling such as Promptfoo and Langfuse, plus tracebased observability and output validation via API/OpenTelemetry integrations.\nPerformance and benchmark testing experience.\nWebSocket testing experience.\nSecurity and dependency scanning awareness.\nExperience in legal-tech, fintech, or other regulated 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