Quality and Validation Engineer

Quality and Validation Engineer

Requirements

• Bachelor’s or Master’s degree in Biomedical Engineering, Electrical Engineering, Computer Science, or a related field.
• Hands-on knowledge of Python programming language, test frameworks, and git
• Hands-on experience with test automation tools and CI/CD like Jenkins, Github Actions
• 3+ years of experience in quality assurance, validation, or regulatory compliance within the medical device or healthcare industry.
• Experience with software verification and validation for AI/ML-based medical applications.
• Hands-on experience with statistical analysis tools and validation methodologies.
• Excellent problem-solving skills and attention to detail.
• Strong technical writing skills for regulatory documentation.
• Ability to work effectively in a cross-functional team environment.

Responsibilities

• Develop and implement V&V strategies for AI-based medical imaging products to meet FDA and other regulatory standards.
• Design, execute, and document test plans, protocols and reports (IQ, OQ, PQ) to QA software components and systems.
• Collaborate with AI researchers, software engineers, and regulatory teams to ensure seamless integration of quality and compliance processes.
• Conduct risk assessments, failure mode analyses, and root cause investigations to enhance product reliability.
• Maintain documentation in compliance with ISO 13485, 14971, IEC 62304, 62366 and other relevant medical device regulations.
• Establish and monitor quality control processes, including software verification and validation activities.
• Support regulatory submissions by providing test documentations, risk analyses, and other compliance documentation.
• Participate in internal and external audits, ensuring adherence to quality management system (QMS) requirements.
• Drive continuous improvement initiatives to enhance testing efficiency and regulatory compliance.

Preferred Qualifications

• Knowledge of FDA regulations, including 21 CFR Part 820, 21 CFR Part 11, and ISO 13485.
• Experience with machine learning model performance testing and algorithmic bias assessment.
• Knowledge of Good Automated Manufacturing Practice (GAMP 5) and computer system validation (CSV).
• Previous experience with software development lifecycle (SDLC) in a regulated environment.
• Familiarity with risk management principles (ISO 14971) and medical imaging standards (DICOM, PACS).
• Familiarity with cybersecurity considerations for medical AI applications.