Sr. Platform Engineer

The Mayo Clinic Platform AI team is seeking an experienced Senior Platform Engineer to join our innovative efforts in developing and implementing cutting-edge generative AI solutions. In this role, you will lead the design and development of state-of-the-art generative AI models, establish comprehensive safety guardrails for responsible AI deployment, and drive the creation of autonomous AI agents. You’ll collaborate closely with a diverse team of data scientists, product managers, and engineers as we shape the future of AI applications while ensuring our systems remain safe, ethical, and scalable.

Key Responsibilities

•  Generative AI Model Development: Architect, design, and implement advanced generative AI models and architectures that support varied departmental applications and cutting-edge research initiatives.
• GenAI Safety & Ethics: Develop comprehensive safety guardrails and ethical guidelines to ensure responsible AI development and deployment, incorporating best practices in AI alignment and security.
• Cross-Functional Collaboration: Partner with cross-functional teams to integrate AI solutions seamlessly within the Mayo Clinic Platform, translating business needs into robust technical implementations.
• Autonomous AI Agents: Lead the creation and optimization of intelligent AI agents designed for autonomous decision-making, leveraging techniques in prompt engineering and model fine-tuning.
  System Enhancement: Evaluate and enhance existing generative AI deployments across departmental applications, continually iterating to improve performance, safety, and scalability.
• Performance Optimization: Identify bottlenecks in AI/ML pipelines and propose solutions to improve system performance, efficiency, and scalability.
• Monitoring & Troubleshooting: Develop and maintain observability tools, including logging, monitoring, and alerting, to diagnose and resolve production issues.
• Documentation: Create and maintain technical documentation, including architectural diagrams, API specifications, and onboarding guides for internal and external stakeholders.
• Thought Leadership: Stay updated with the latest trends and advancements in federated learning, distributed computing, and machine learning frameworks to continually enhance the platform.

• Bachelors degree in a relevant information technology field or a minimum 7 years of direct full-stack engineering with increasing complexity.
• 3-5 years working in diverse environments utilizing Agile principles of software development.
• Proven experience as a Full Stack Engineer with a strong emphasis on healthcare interoperability.
• Proficiency in Java and/or .NET for backend development, including API / service design and implementation.
• Expertise in Javascript with a focus on React and react frameworks (e.g. NextJS) for building responsive and intuitive front-end applications.
• Hands-on experience with Google Cloud Platform (GCP) (or equivalent) services and cloud-native application development.
• Familiarity with healthcare interoperability standards such as HL7, FHIR and OMOP.
• Strong problem-solving skills and the ability to work in a collaborative, cross-functional team environment.
• Experience with DevOps practices, CI/CD pipelines, and containerization technologies (e.g., Docker, Kubernetes) is a plus.
• Knowledge of healthcare data security and compliance requirements, including HIPAA, is highly desirable.
• Excellent communication skills and the ability to convey complex technical concepts to non-technical stakeholders.
• A proactive and self-driven mindset with a passion for staying up-to-date with emerging technologies and industry best practices.
• Experience with Interoperability standards such as HL7, FHIR and OMOP.
• Experience with solutions integration/delivery in a healthcare setting.

Preferred Experience

2 years in a senior or lead capacity, ideally in a distributed systems, AI/ML, or large-scale data environment. Programming Skills: Strong proficiency in languages such as Python, Java, C++, or Go, with demonstrated experience building production-grade services. Machine Learning Frameworks: Familiarity with common ML libraries and frameworks (e.g., TensorFlow, PyTorch), especially those supporting federated learning (e.g., TensorFlow Federated). Distributed Systems: Solid understanding of distributed computing principles, including concurrency, data partitioning, and scaling strategies. Cloud & DevOps: Hands-on experience with cloud platforms (AWS, Azure, or GCP) and container orchestration (Docker, Kubernetes). Familiarity