Role Overview
Join a forward-thinking technology team building AI-powered healthcare software that supports critical clinical workflows and improves operational efficiency. 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. Your work will directly contribute to delivering secure, reliable healthcare technology that supports emergency department professionals and their patients.
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.
- Automation framework development: Build, maintain, and optimize automated test frameworks for web applications, APIs, and integrations.
- 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.
- CI/CD integration: Integrate automated testing into continuous integration and delivery pipelines.
- Defect management and root-cause analysis: Identify, document, prioritize, and resolve defects in collaboration with engineering teams.
- Exploratory and agile testing: Lead exploratory testing for new features and complex clinical workflows.
- AI-assisted test engineering: Use large language models to transform user stories, acceptance criteria, and clinical specifications into test scenarios.
- Agentic test automation: Leverage AI coding agents, Playwright MCP, and automated planning, generation, and healing capabilities.
- Intelligent failure analysis: Apply AI-assisted locator healing, flaky-test detection, failure clustering, root-cause summaries, and defect routing.
- Synthetic test data management: Establish realistic, privacy-safe synthetic patient records and clinical scenarios.
- AI feature evaluation: Build evaluation frameworks for AI-powered capabilities using golden datasets, scoring rubrics, and model-based grading.
- AI safety and security: Test AI features for hallucinations, clinical and coding inaccuracies, bias, privacy leakage, prompt injection, and unsafe behavior.
- Quality metrics and continuous improvement: Track escaped defects, test flakiness, execution times, test authoring efficiency, and maintenance effort.
- Security, accessibility, and compliance: Incorporate security scanning, accessibility checks, dependency analysis, and healthcare data protection requirements.
- Knowledge sharing and collaboration: Work closely with developers, product stakeholders, and clinical specialists to establish a quality-first culture.
What we offer
- Competitive compensation: Salary aligned with UK working hours, with an emphasis on fair and transparent compensation.
- Remote flexibility: Work remotely with flexibility designed to support work-life balance.
- Global collaboration: Work alongside talented professionals from diverse backgrounds and locations.
- Professional development: Access training opportunities to strengthen technical skills and expand expertise in QA, automation, and AI-driven quality engineering.
- Career progression: Opportunities for long-term professional growth and advancement within the organization.
- Healthcare coverage: Comprehensive health benefits designed to support employee well-being and peace of mind.
- Technical innovation: Opportunity to shape a modern QA practice and work with AI coding agents, advanced automation frameworks, and AI evaluation technologies.
- Meaningful impact: Contribute to the reliability and safety of healthcare software supporting critical clinical workflows.
Requirements
- Professional experience: At least 5 years of professional QA or software testing experience, including a minimum of 3 years developing and maintaining automated testing solutions.
- QA fundamentals: Strong understanding of testing methodologies, risk-based testing, test design techniques, defect lifecycle management, and software development lifecycle practices.
- Programming skills: Strong proficiency in TypeScript or JavaScript and working knowledge of Python.
- Test automation expertise: Hands-on experience with modern automation frameworks, preferably Playwright, with Cypress or Selenium also considered.
- AI-native engineering practices: Regular, practical use of AI coding agents in test engineering.
- Testing AI-powered systems: Understanding of how large language model applications can fail and experience evaluating non-deterministic outputs.
- Performance testing: Practical experience with Locust, k6, JMeter, or comparable tools.
- API and integration testing: Solid experience testing APIs and integrations.
- CI/CD and version control: Experience integrating automated test suites into build pipelines and working with CI/CD tooling and Git-based development workflows.
- Security and data quality awareness: Understanding of secure testing practices, test data management, and the importance of protecting sensitive information in healthcare-related environments.
- Communication and ownership: Strong analytical and problem-solving skills, a proactive approach to identifying risks.
- Remote and agile collaboration: Experience working in fast-paced, agile, distributed teams.
- Education: Bachelor’s degree in Computer Science, Software Engineering, or a related discipline, or equivalent practical experience.
Nice to have
- Experience in healthcare technology or regulated clinical software (EHR, EDIS, ONC Health IT).
- Familiarity with HL7 v2, FHIR, and HIPAA requirements.
- Hands-on experience with LLM evaluation and AI observability tools (Promptfoo, DeepEval, Ragas, Langfuse, or LangSmith).
- Experience building MCP servers, custom AI agents, or AI-assisted engineering tools.
- Knowledge of Playwright MCP and AI-driven test planning.
- Experience with performance tools (Locust or k6) and security tools (OWASP ZAP).
- Experience with accessibility testing, WCAG standards, and axe-core.
- Familiarity with AI security testing, prompt-injection assessment, and the OWASP Top 10 for LLM Applications.
- Experience with contract testing tools such as Pact.
