Senior QA Engineer
Pubblicato il 23-10-2026 - Jobgether in Italia
This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior QA Engineer based in Italy.
Join a forward-thinking technology team building AI-powered healthcare software that supports critical clinical workflows and improves operational efficiency.
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In this role, you will establish and lead a next-generation quality engineering practice, combining modern test automation with AI-driven testing methodologies.
You will take ownership of the QA strategy, architect the testing infrastructure, and build scalable frameworks that ensure software reliability, security, and performance.
Working with AI coding agents and advanced evaluation tools, you will help validate both traditional software functionality and the accuracy, safety, and dependability of AI-powered features.
You will collaborate closely with engineers, product managers, designers, and clinical experts to translate complex requirements into measurable quality standards.
Your work will directly contribute to delivering secure, reliable healthcare technology that supports emergency department professionals and their patients.
This is an exciting opportunity for a hands-on quality engineering leader who values ownership, continuous improvement, and practical AI innovation.
Accountabilities
- QA strategy and quality leadership: Own the end-to-end quality assurance strategy, defining test plans, risk-based coverage targets, quality gates, and release readiness criteria across the software development lifecycle.
- Test architecture and tooling: Evaluate, select,
and document the testing technology stack for user interfaces, APIs, integrations, performance, accessibility, and security. Record architectural decisions, alternatives, and trade-offs to establish a scalable, maintainable approach.
- Automation framework development: Build, maintain, and optimize automated test frameworks for web applications, APIs, and integrations. Establish clear standards that enable engineers and AI agents to extend test coverage reliably.
- Clinical workflow validation: Replace manual regression testing with automated coverage of critical healthcare workflows, including triage, clinical documentation, orders, and patient discharge.
- Performance and reliability testing: Develop load, stress, and soak tests that simulate realistic emergency department usage and peak traffic. Establish performance baselines and identify regressions before production releases.
- CI/CD integration: Integrate automated testing into continuous integration and delivery pipelines so that pull requests and builds receive fast, reliable feedback. Apply parallel execution and AI-assisted test impact analysis to improve testing efficiency.
- Defect management and root-cause analysis: Identify, document, prioritize, and resolve defects in collaboration with engineering teams.
Investigate underlying causes and implement improvements that prevent recurring issues.
- Exploratory and agile testing: Lead exploratory testing for new features and complex clinical workflows. Contribute to sprint planning, backlog refinement, retrospectives, acceptance criteria, effort estimation, and release readiness decisions.
- AI-assisted test engineering: Use large language models to transform user stories, acceptance criteria, and clinical specifications into test scenarios, edge cases, and boundary conditions. Review and refine AI-generated outputs using sound testing principles and engineering judgment.
- Agentic test automation: Leverage AI coding agents, Playwright MCP, and automated planning, generation, and healing capabilities to create and maintain tests. Ensure AI-generated code meets the same quality and review standards as production code.
- Intelligent failure analysis: Apply AI-assisted locator healing, flaky-test detection, failure clustering, root-cause summaries, and defect routing to reduce maintenance effort and accelerate issue resolution.
- Synthetic test data management: Establish realistic, privacy-safe synthetic patient records and clinical scenarios as the standard for functional, performance, and AI evaluation testing. Use appropriate generation tools while ensuring that real protected health information is never exposed in test environments or AI prompts.
- AI feature evaluation: Build evaluation frameworks for AI-powered capabilities using golden datasets, scoring rubrics, model-based grading calibrated against human reviews, and regression gates triggered by changes to models, prompts, or knowledge sources.
- AI safety and security: Test AI features for hallucinations, clinical and coding