Senior QA Engineer

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 Canada. 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. 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 inaccuracies, bias, privacy leakage, prompt injection, and unsafe behavior. Partner with clinical experts to define measurable acceptance thresholds for safe and reliable use in emergency care. • Quality metrics and continuous improvement: Track escaped defects, test flakiness, execution times, test authoring efficiency, and maintenance effort. Communicate quality trends and demonstrate the impact of AI-assisted engineering practices. • Security, accessibility, and compliance: Incorporate security scanning, accessibility checks, dependency analysis, and healthcare data protection requirements into testing workflows. • Knowledge sharing and collaboration: Work closely with developers, product stakeholders, and clinical specialists to establish a quality-first culture and share reusable testing practices across engineering teams. 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, ideally in a SaaS or web application environment. • 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. Ability to write maintainable, reliable test code to the same standards expected of production software. • Test automation expertise: Hands-on experience with modern automation frameworks, preferably Playwright, with Cypress or Selenium also considered. Demonstrated ability to build frameworks from the ground up, manage test data, maintain stable and efficient suites, and make well-reasoned tooling decisions. • AI-native engineering practices: Regular, practical use of AI coding agents in test engineering, with concrete examples of how these tools have improved test creation, maintenance, or debugging. Ability to identify inaccurate, incomplete, or unreliable AI-generated output. • Testing AI-powered systems: Understanding of how large language model applications can fail and experience evaluating non-deterministic outputs through evaluation datasets, scoring rubrics, model-based grading, and measurable pass thresholds. • Performance testing: Practical experience with Locust, k6, JMeter, or comparable tools, including designing realistic workload models, interpreting performance results, and identifying bottlenecks. • API and integration testing: Solid experience testing APIs and integrations, with the ability to validate contracts, data exchanges, and system behavior across interconnected services. • 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, and the ability to communicate quality concerns clearly to engineers, product managers, and leadership. • Remote and agile collaborat