Lead Applied AI Architect

Tampa General Hospital•Tampa, FL

About The Position

The Lead Applied AI Architect is Tampa General Hospital’s enterprise design authority for applied AI. Working within the Office of the Chief Medical Informatics and AI Strategy Officer and in partnership with other IT divisions, this role establishes the reference architecture, engineering standards, and technical roadmap for internal AI development. The architect is accountable for architectural decisions governing how AI agents and large language model (LLM)-based solutions are designed, built, integrated, and operated, and provides final technical sign-off before solutions proceed to independent validation and governance review. The role combines enterprise architecture accountability with hands-on engineering. It directs the technical work of Applied AI Engineers, reviews vendor and third-party AI solutions, and advances AI capabilities from prototype to governed production in partnership with data science, informatics, cybersecurity, and clinical leaders. The position is responsible for performing duties in accordance with Tampa General Hospital’s mission, vision, and values.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Science, Engineering, Mathematics, or a related field.
At least one relevant cloud, machine learning, or software engineering certification.
Ten years of professional engineering experience, including at least four years designing and building AI/ML systems and at least two years architecting, deploying, and operating LLM-based or agentic applications in production.
Prior experience as a technical lead or architect with design authority across multiple teams, including code review and mentorship.
Experience with an enterprise data/development platform such as Palantir Foundry or a similar platform.
Advanced proficiency in Python, SQL, APIs, distributed application design, automated testing, version control, and production AI application development.
Deep knowledge of responsible and secure AI, including evaluation, guardrails, privacy, security, threat modeling, access control, auditability, and human-in-the-loop design.
Ability to define enterprise reference architectures, reusable patterns, engineering standards, technical roadmaps, and build-versus-buy recommendations.
Experience with CI/CD, MLOps/LLMOps, observability, incident response, rollback, reliability, lifecycle management, and cost optimization.
Ability to design end-to-end automated workflows across enterprise applications, APIs, document repositories, and human approval steps.
Ability to make and defend enterprise architectural decisions, explain tradeoffs to executive and clinical leaders, direct and mentor engineers, and balance innovation, safety, value, and delivery.
Effective communication across technical and clinical audiences.
Working knowledge of clinical workflows, PHI handling, APIs, enterprise data platforms, and healthcare interoperability standards such as HL7/FHIR.

Responsibilities

Lead the design, development, and optimization of AI agent architectures for innovative and scalable solutions.
Drive full-cycle machine learning model deployment, including data preprocessing, training, testing, and production integration.
Oversee development and implementation of algorithms for natural language understanding, decision-making, and contextual reasoning.
Collaborate with cross-functional teams to integrate AI platforms with existing enterprise systems.
Mentor and guide engineers and data scientists to improve technical skills and operational efficiency.
Lead continuous improvement efforts, including model retraining, A/B testing, and performance evaluation.
Track emerging AI and machine learning trends and adapt solutions to business needs.
Establish and enforce best practices for code quality, documentation, and version control in AI projects.
Conduct regular reviews and present findings or developments to stakeholders and senior leadership.
Manage resources and prioritize tasks to meet deadlines and strategic objectives.
Ensure ethical AI practices and compliance with applicable legal standards in development and deployment.
Identify opportunities to use AI to address complex business challenges and improve operational efficiency.
Set the reference architecture, engineering standards, and technical roadmap for internal AI development.
Provide final technical sign-off before solutions enter independent validation and governance review.
Direct the technical work of Applied AI Engineers and serve as technical reviewer for vendor and third-party AI.
Move AI capabilities from prototype to governed production in partnership with data science, informatics, cybersecurity, and clinical leaders.