{"id":124395,"url":"https://alion.io/job/zeroto1-qa-engineer","title":"QA Engineer","company":{"id":3193,"name":"Zeroto1","domain":"zeroto1.ai","url":"https://alion.io/company/zeroto1","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"QA","role_family":"QA","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"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":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Function Calling","optional":false},{"name":"JavaScript","optional":false},{"name":"Lighthouse","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TypeScript","optional":false}],"status":"live","first_seen_at":"2026-08-26T05:36:42Z","employer_posted_date":null,"last_verified_at":"2026-08-26T05:36:42Z","board_verified":false,"closed_at":null,"days_open":51,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":51},"description":"Responsibilities:\nDesign the end-to-end quality strategy for Lighthouse and the AI systems built on it, covering APIs, web experiences, data and context pipelines, agent and workflow orchestration, enterprise integrations, permissions, and deployment paths.\nBuild and maintain automated API, integration, contract, UI, and regression suites with meaningful CI/CD quality gates; decide what should be validated with deterministic assertions and what requires evaluation-based testing.\nTest agentic behavior beyond happy-path prompts: multi-step reasoning and workflows, tool selection and use, structured outputs, retrieval and context grounding, state and memory, human approvals, retries, fallbacks, and recovery from partial failure.\nCreate evaluation datasets, golden cases, rubrics, thresholds, and error taxonomies for model and agent behavior so prompt, model, context, tool, and workflow changes can be measured before release.\nActively test high-risk failure modes, including hallucinated or unsupported outputs, prompt injection, unsafe tool actions, permission bypass, tenant leakage, malformed inputs, schema drift, rate limits, timeouts, duplicate or delayed events, and degraded upstream systems.\nValidate enterprise integrations and data quality across databases, warehouses, documents, APIs, files, events, and MCP; verify authentication, authorization, freshness, provenance, traceability, and behavior under unreliable dependencies.\nOwn release readiness, exploratory testing, defect triage, root-cause analysis, and production quality feedback. 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