We are looking for a Senior QA Engineer with deep expertise in healthcare processes and AI driven applications. This role is critical in ensuring the safety, accuracy, and compliance of our healthcare-focused AI products. The ideal candidate will have a strong background in QA methodologies (both manual and automated testing, building test cases, writing test plans, etc.), experience working with regulated health data (e.g., HIPAA), and the ability to validate AI/ML models used in clinical or operational healthcare settings.
Required Qualifications:
5+ years of experience in software QA, with 2+ years in healthcare technology.
Experience testing AI/ML-based systems, including model performance validation and risk analysis.
Strong understanding of healthcare data formats (e.g., HL7, FHIR), workflows, and EHR systems.
Familiarity with HIPAA, HITECH, and relevant healthcare data privacy/security regulations.
Healthcare domain experience – Familiarity with healthcare workflows or standards is essential.
5+ years of manual testing experience, specifically with Web and/or Mobile applications – Including experience in:
Web automation experience (e.g., Selenium, Katalon)
Basic understanding of FHIR or patient data handling
Key Responsibilities:
Design, develop, and maintain comprehensive test strategies and test plans for AI-powered healthcare solutions.
Ensure compliance with healthcare regulations and standards, such as HIPAA, HL7, FHIR, 21 CFR Part 11, and ISO 13485 (as applicable).
Validate AI/ML model behavior for clinical accuracy, data bias, and safe integration with EHR or patient-facing applications.
Test complex healthcare data pipelines for correctness, consistency, and data privacy requirements.
Collaborate with data scientists, product managers, scrum masters, and BAs to define and verify model validation criteria and system quality gates.
Lead risk based testing approaches tailored to clinical safety and patient data sensitivity.
Implement and maintain automated testing frameworks, including for backend APIs, data validation, and AI inference results.
Develop synthetic clinical datasets and edge cases to assess robustness and fairness of AI models.
Participate in product design reviews to provide QA perspective on usability, validation, and regulatory readiness.
Preferred Qualifications:
Experience with clinical decision support systems (CDSS) or AI-based diagnostic tools.
Familiarity with model explainability, bias testing, and responsible AI frameworks.
Soft Skills:
High attention to detail and a strong sense of accountability.
Ability to communicate effectively with both technical and non-technical stakeholders.
Proactive, self-driven, and committed to delivering high-quality, compliant solutions.
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