AI/ML Ops Engineer

Design and Development: 

Designs, builds, and deploys machine learning models. 

Develops algorithms that can learn and make predictions or decisions. 

Conducts model training, evaluation, and tuning to achieve optimal results. 

Implementation: 

Implements machine learning models into production environments, ensuring they meet medical device regulatory standards. 

Monitors and maintains the performance of deployed models, focusing on patient safety and compliance. 

Collaboration and Documentation: 

Collaborates with data scientists to refine and improve model accuracy within clinical settings. 

Writes detailed documentation for machine learning algorithms, model training, and evaluation processes, ensuring clarity for regulatory audits. 

Details model deployment workflows and maintenance procedures, including compliance checks. 

Testing and Validation: 

Conducts extensive testing of machine learning models, including unit tests and integration tests, to validate clinical efficacy and safety. 

Validates models in deployment environments (e.g., real-world clinical settings) to ensure they perform as expected under real-world conditions. 

Conducts root cause analysis for model performance drops or inconsistencies, with a focus on clinical outcomes. 

Monitoring and Improvement: 

Monitors models post-deployment for drift and retrains them as necessary to maintain clinical performance and safety. 

Debugs problems in machine learning algorithms, training processes, and model deployment, with an emphasis on patient safety and regulatory compliance. 

Implements solutions to fix bugs, optimize model training, and improve deployment robustness, adhering to medical device standards. 

Regulatory Compliance and Risk Management: 

Ensures all machine learning development and deployment efforts comply with relevant regulations (e.g., FDA, MDR, ISO 13485, ISO 14971). 

Participates in risk assessments, identifying and mitigating potential risks associated with the machine learning model. 

Supports the creation of submission documents necessary for regulatory approvals. 

Ethical and Privacy Considerations: 

Ensures that the development and deployment processes respect patient privacy, complying with data protection regulations such as GDPR and HIPAA. 

Monitors for biases in the machine learning models to ensure equitable and unbiased patient care.