SENIOR AI QUALITY ENGINEER (E-TD14)
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- The work spans three distinct problems, and you keep them clear: testing agentic product behaviour, using agents to test conventional software, and building the agentic quality infrastructure that supports both.
- Quality engineering
- Design and maintain automated test suites across your team's interfaces and surfaces.
- Write integration and contract tests that validate the interfaces your team provides to others — focused on correctness, schema stability, and clear failure isolation.
- Bring advanced quality practices where they earn their place: property-based testing, fuzzing, mutation testing, contract testing, non-functional testing, and production-quality signals.
- Assess non-functional risks: performance regressions, data consistency, access control, sensitive-data handling, migration safety, backward compatibility.
- Investigate failing builds: triage root cause, distinguish product bugs from test brittleness, clarify ownership across team boundaries.
- Work with Claude, stay accountable
- Use Claude Code as your primary collaborator for generating tests, investigating failures, proposing fixes, and drafting multi-file change sets.
- Write briefs, acceptance criteria, and CLAUDE.md conventions that Claude can execute against without further clarification; use subagents and MCP tools to go beyond single-shot prompting.
- Build evals and headless agent runs in CI that catch regressions automatically — not just tests a human triggers.
- Review and approve Claude-generated test suites before they merge — verify correctness, coverage, traceability to intended behaviour, and that assertions were not weakened just to pass; escalate suspicious changes and require human approval for any change to expected behaviour.
- Build reusable prompting patterns and context conventions for test generation in the codebase.
- Contribute to the quality journey
- Share what works in agentic quality engineering through demos, retros, documentation, and conversation.
- Contribute to shared quality standards: validation artefacts on AI-assisted PRs, CI quality gates, coverage expectations.
- Surface where agentic approaches could reduce test toil or improve coverage.
- Help engineers own quality and move the team up the testing-maturity curve — beyond manual and AI-assisted test writing toward bounded agents that observe changes, write and repair tests, and help keep suites green — matching autonomy to risk and never lowering the quality bar.
- Technical environment:
- Claude Code, subagents, MCP integrations and multi-agent workflows
- Python, TypeScript, Vue, Postgres, Azure, Kubernetes and Terraform
- Cloud-native, service-oriented architecture
- 5+ years in test or quality engineering with ownership of automated test suites across a team or service.
- Strong API and integration testing skills — the team's output is almost entirely interfaces.
- Hands-on Claude Code experience — test generation, failure analysis, multi-file editing; not just autocomplete.
- CI/CD fluency — writing and maintaining quality gates, not just running them.
- Writes acceptance criteria that leave no ambiguity for implementation or for Claude to execute against.
- Comfort with data-adjacent testing: databases, data integrity, consistency, and API contracts.
- Fluent English for clear communication with engineers, product stakeholders, and other teams.
- Optionally work in the Neatherlands or Hungary
- Contract testing or consumer-driven contract patterns.
- SQL for test-data setup and assertion.
- Security-aware testing: secrets management, sensitive-data handling, compliance contexts.
- Search-engine testing: relevance, edge cases, performance.
- Experience contributing to a team's AI tooling adoption.