Senior Platform Engineer
United States of America
Full Time
3 weeks ago
$128,000 - $186,000 USD
No H1B
Key skills
Full Stack EngineeringHealthcare InteroperabilityJavaDot NETAPI DesignJavaScriptReactNextJSGoogle Cloud PlatformCloud-Native Application DevelopmentHL7FHIROMOPDevOps PracticesCI/CD PipelinesContainerizationDockerKubernetesHealthcare Data SecurityHIPAA ComplianceLLM Fine-TuningPrompt EngineeringAI AlignmentAI Safety MechanismsRetrieval-Augmented GenerationAI Model DeploymentCloud-Based SolutionsMulti-Modal AI Systems.NETAIMachine LearningMLGenerative AIGenAILarge Language ModelsRAGGCPGoogle CloudPerformance OptimizationAgileCI/CDLeadershipCommunicationCollaboration
About this role
Mayo Clinic Healthcare is a renowned organization seeking a Senior Platform Engineer to join their AI team. This role involves leading the design and development of generative AI models, establishing safety guidelines, and collaborating with cross-functional teams to integrate AI solutions.
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
Requirements:
- 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
- Experience in fine-tuning large language models and advanced prompt engineering techniques
- Hands-on background in AI alignment strategies and the development of robust safety mechanisms
- Familiarity with Retrieval-Augmented Generation (RAG) techniques to improve model responsiveness and efficiency
- Proven experience with AI model deployment and scaling in production environments, including container orchestration and cloud-based solutions
- Understanding and experience working with multi-modal AI systems that integrate text, image, and other data types