Senior Test Engineer (Remote Opportunity)

VetsEZ is currently looking for a Senior Test Engineer for a 100% remote position supporting a large federal government healthcare modernization project. In this role, you will support the development, validation, and automation of an AI-powered Patient Health Data Summarization capability within the Joint Longitudinal Viewer (JLV) Clinical Decision Support (CDS) platform. The ideal candidate will have strong healthcare interoperability experience, knowledge of C-CDA standards, experience testing complex healthcare applications, and hands-on experience with test automation and AI-enabled quality engineering.

The candidate must reside within the continental US.

Responsibilities

• Develop comprehensive test plans, test cases, and acceptance criteria for AI-powered patient summarization capabilities.
• Create traceability between business requirements, clinical data standards, and test scenarios.
• Design test coverage across clinical document types, including Continuity of Care Documents (CCD), Discharge Summaries, Progress Notes, and History & Physical documents.
• Develop positive, negative, boundary, and edge-case testing scenarios for AI-enabled clinical applications.
• Analyze and validate C-CDA XML documents, including headers, sections, templates, and clinical entries.
• Verify accurate extraction and summarization of clinical information, including medications, allergies, laboratory results, procedures, care plans, and diagnoses.
• Ensure AI-generated summaries accurately reflect source clinical documentation and maintain traceability to original clinical sources.
• Perform functional, integration, system, regression, performance, and user acceptance testing.
• Develop and maintain automated test suites for document ingestion, AI summarization, APIs, and user interfaces.
• Test CDS workflows and patient-context-driven launch scenarios across multiple data sources and patient encounters.
• Support CI/CD pipelines through automated quality gates and continuous testing.
• Build automated validation frameworks for AI-generated summaries.
• Evaluate AI-generated content for clinical completeness, consistency, usability, accuracy, and reliability.
• Identify omissions, inaccuracies, hallucinations, and other AI-generated defects that could impact clinical workflows.
• Validate AI guardrails, monitoring capabilities, auditability, and quality controls.
• Participate in defect triage, root-cause analysis, and release-readiness activities.
• Create test documentation, defect reports, traceability matrices, and quality assessments.
• Collaborate with software engineers, solution architects, clinicians, product owners, cybersecurity teams, and government stakeholders.
• Leverage AI-assisted tools to accelerate test development, test automation, regression testing, defect analysis, and quality assurance activities.
• Take on additional tasks and responsibilities as needed to support team objectives and ensure the success of the project.

Requirements

• Bachelor’s degree in Computer Science, Information Technology, Health Informatics, Engineering, or a related technical discipline, or equivalent experience.
• Minimum of 5 years of experience testing healthcare software applications.
• Strong knowledge of Consolidated Clinical Document Architecture (C-CDA) standards, including clinical document structure, sections, templates, and entries.
• Hands-on experience reading, validating, and troubleshooting XML-based healthcare data.
• Experience with healthcare interoperability standards and clinical data exchange.
• Experience testing Clinical Decision Support (CDS) applications and workflows.
• Familiarity with SMART on FHIR, CDS Hooks, REST APIs, and related interoperability technologies.
• Experience with test automation frameworks, API testing tools, and defect management platforms.
• Experience developing and maintaining automated testing solutions within Agile and DevSecOps environments.
• Experience with AI-assisted testing tools and techniques for test case generation, automation development, regression testing, defect analysis, and quality assurance.
• Familiarity with Generative AI and Large Language Models (LLMs), including using AI to create and maintain automated test scripts, validation frameworks, and test data.
• Experience validating AI-generated outputs and establishing quality controls for accuracy, consistency, repeatability, and reliability.<