AI Engineering Senior Principal

About Premise Health

Premise Health is a company dedicated to providing exceptional healthcare to large organizations and their people.

About the Role

As the AI Engineering Senior Principal, you will lead the AI engineering initiatives, defining and implementing enterprise-wide AI architecture and best practices while ensuring security and compliance across all AI systems.

Responsibilities

• Define and lead the enterprise AI architecture strategy for Premise Health, establishing architectural principles, standards, patterns, and best practices for AI/ML solutions across the organization
• Design and architect secure, scalable AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and other advanced AI technologies while ensuring alignment with business objectives
• Establish and enforce information security architecture for all AI systems, including Identity and Access Management (IAM), computer-to-computer authentication mechanisms, API security, data protection, and compliance with healthcare regulations (HIPAA, HITECH)
• Lead the creation of reference architectures for AI-powered applications that serve as reusable templates and accelerate development across multiple teams and business units
• Partner with business and IT leaders to make investment decisions that balance current operational demands with long-term strategic vision for AI capabilities and innovation
• Architect cloud-native AI solutions on Azure, AWS, and other cloud platforms, establishing patterns for microservices, serverless computing, event-driven architectures, and containerization
• Design and implement secure API gateway strategies for both public and private APIs, ensuring proper authentication, authorization, rate limiting, and monitoring across all AI services
• Establish DevOps and CI/CD practices for AI/ML solutions, ensuring continuous delivery, automated testing, model versioning, and deployment pipelines for AI systems
• Lead cross-functional collaboration efforts between data science, engineering, security, and operations teams to deliver integrated AI solutions that meet business requirements
• Evaluate and recommend emerging AI technologies, frameworks, and platforms based on business value drivers, return on investment, and alignment with enterprise architecture standards
• Develop and maintain technology roadmaps for AI capabilities, identifying opportunities for automation, process improvement, and innovation using artificial intelligence and machine learning
• Architect healthcare-specific AI solutions with expertise in health data interchange standards (HL7, FHIR) and healthcare workflow automation using AI and RPA technologies
• Establish governance frameworks for responsible AI, including ethical AI principles, bias detection and mitigation, model explainability, and compliance with regulatory requirements
• Lead the architecture team for AI initiatives, including recruitment, development, mentoring, and performance management of architects and senior technical staff
• Create and maintain comprehensive architectural documentation including solution architectures, technical specifications, integration patterns, security models, and decision records
• Conduct architectural reviews and provide technical guidance to development teams working on AI-powered services, products, and platforms
• Design multi-tenant SaaS architectures for AI applications that support multiple regions, ensure data isolation, and provide accurate cost allocation and billing
• Establish architectural patterns for integrating AI capabilities into existing systems, including event sourcing, pub/sub messaging, claim check patterns, sidecar patterns, and gateway patterns
• Lead proof-of-concept and pilot initiatives to validate new AI technologies and architectural approaches before enterprise-wide adoption
• Collaborate with information security teams to ensure all AI solutions meet security requirements for authentication, authorization, encryption, audit logging, and threat protection
• Develop communication strategies and materials to evangelize AI architecture principles and best practices across the organization at all levels from technical teams to executive leadership
• Advise executive leadership on AI strategy, technology options, risks, costs versus benefits, system impacts, and implementation priorities
• Drive migration strategies to transition legacy systems to modern AI-enabled cloud-native architectures with minimal disruption to business operations
• Establish practices for resource allocation, cost optimization, and performance monitoring for cloud-based AI solutions
• Champion agile ways of working and modern development practices across AI engineering teams, including iterative development, continuous improvement, and DevOps culture
• Ensure AI solutions are architected for high availability, disaster recovery, scalability, performance, and operational