Senior AI/ML Engineer (Hybrid)
HybridRochester, Minnesota, United States
Job Summary
Lead component design, development, and integration of AI-driven solutions for clinical practice, leveraging deep learning, natural language processing, and computer vision to deploy end-to-end healthcare applications. Collaborate with clinicians, designers, and IT professionals to assess feasibility, translate requirements into design concepts, and establish evaluation methodologies for AI effectiveness. Oversee engineering systems for regulatory compliance, maintain CI/CD pipelines for automated releases, and implement best practices for AI development standards. Provide technical leadership, mentor junior engineers, and offer consultative services to clinical work units by explaining complex data insights to non-technical users. Deliver training on AI tools and contribute to advancing state-of-the-art healthcare AI methods.
Required Qualifications
- Experience in the clinical environment, including workflows, challenges, and requirements of healthcare providers and patients
- Ability to leverage advanced techniques in AI/ML to analyze vast amounts of healthcare data, including patient records, medical imaging, and genomic information
- Ability to develop, integrate, and standardize software components and create, maintain, and follow quality system procedures
- Ability to guide the engineering of systems that are pivotal to developing and deploying AI solutions
- Ability to facilitate consistent and automated AI software solution development and releases through the design, testing, and maintenance of tools and associated CI/CD pipelines
- Ability to communicate complex findings in easily understandable terms to non-technical users
- Knowledge of machine learning techniques such as deep learning, natural language processing, computer vision, and large language models
- Ability to establish evaluation methodologies and performance metrics to assess AI solutions' effectiveness, usability, and impact in real-world healthcare settings
- Ability to explain data analysis results to guide strategic choices and clarify complex insights for non-technical users
- Ability to provide mentorship, guidance, and technical leadership to junior engineers within the AI enablement team
- Ability to provide consultative services on areas of expertise to clinical work units or AI product teams
- Ability to provide training and education to healthcare staff on AI tools and technologies
- Ability to contribute to developing new AI methods and technologies that can advance the state-of-the-art in healthcare AI
- Understanding of regulatory requirements for digital health technology products
- Knowledge of design requirements, development, component creation, verification, non-clinical validation, and risk mitigation for AI software solutions
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