Enterprise AI Architect

Summary:

The mission of The University of Texas M. D. Anderson Cancer Center is to eliminate cancer in Texas, the nation, and the world through outstanding programs that integrate patient care, research, prevention, and education. Core to the success of our mission is the ability to orchestrate multidimensional data, data analytics, and machine learning to create sustainable impact within a framework of responsible AI. We are building a dynamic team of machine learning engineers and data scientists that can help us consistently and responsibly accelerate the impact of AI across the enterprise, driving long-lasting improvements in cancer care.

Join the world's premier cancer center as the visionary Enterprise AI Architect, where you will strategically lead the design and implementation of comprehensive AI/ML architectures across the organization. Your expertise will ensure scalable, secure, and compliant AI capabilities that empower transformative innovations in clinical care, operations, research, and education.
In this pivotal role, you will shape our institutional AI strategy, establish robust and rigorous AI lifecycle management framework, and lead the development of an integrated AI ecosystem combining advanced models, data infrastructure, and commercial platforms. Leveraging hybrid cloud and on-premise infrastructures, you'll embed rigorous AI governance and operations to guarantee trustworthy, auditable, and impactful AI solutions at scale, ultimately driving measurable outcomes across the enterprise.

****Ideal candidates will be Epic certified****

Key Responsibilities Include:

Architect and govern scalable, secure AI/ML platforms aligned with institutional strategy.
collaborate with other institutional architects to ensure alignment across the digital operations ecosystems, promoting consistency and synergy.
Lead multidisciplinary teams to implement AI/ML infrastructure supporting development, deployment, monitoring, and auditability.
Integrate AI/ML platforms with HealthIT (Epic, PACS, LIS) and enterprise applications using FHIR, HL7, and DICOM standards.
Collaborate with cybersecurity and compliance teams to enforce zero-trust security, IAM, encryption, and more to ensure HIPAA compliance.
Optimize hybrid cloud/on-premises infrastructure leveraging Azure services.
Provide strategic guidance on vendor partnerships, platform selection, and integration decisions.
Support the implementation of AI lifecycle management, with an emphasis on model monitoring, audit trails, and safety/impact tracking to ensure positive clinical, operational, and financial outcomes.

Technical Expertise
Deep understanding of AI lifecycle Management, AI/ML Platform architecture.
Knowledge of EPIC, PACS, LIS, and other HealthIT/business systems including ServiceNow, SalesForce, etc.
Strong understanding of enterprise architecture frameworks (, TOGAF).
Expertise in AI/ML platform infrastructure, including Kubernetes, NVIDIA DGX, and Azure.
Experience integrating AI/ML platforms, solutions, models with healthcare IT and business systems.
Familiarity with healthcare interoperability and compliance standards: HL7, FHIR, DICOM, HIPAA, FDA guidelines.
Extensive hands-on experience with Azure cloud services including Azure ML, AKS, Azure DevOps, and Azure Data Factory.
Proficiency in DevOps, CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes).
Skilled in network and security architecture, including zero-trust models, IAM, encryption, and secure data transit.
Experience implementing AI model monitoring, audit trails, and impact measurement frameworks.
Knowledge of enterprise data platforms (, Azure Fabric), data pipelines, and data governance frameworks to support AI/ML.

Analytical Skills
Skilled in translating clinical, operational, and strategic business requirements into robust AI architectures.
Experienced in comprehensive risk assessment related to security, compliance, and ethical AI deployment.
Capable in performing quantitative evaluations for AI infrastructure planning and vendor selections.
Familiarity with ISO/IEC standards relevant to AI (, ISO/IEC 42001, ISO/IEC 23894), IHE profiles, and other external standards organizations and frameworks critical to the safe and compliant development and deployment of AI/ML systems in healthcare.

Professionalism: Oral and Written
Proven ability to foster collaborative decision-making processes with clinical, technical, and executive teams.
Exceptional skill in communicating complex AI strategies and architectures effectively to stakeholders.
Adept in crafting detailed yet accessible documentation to facilitate governance, compliance, and clarity.
Effective in preparing strategic reports highlighting impacts, risks, and recommendations clearly for executive stakeholders.

Other duties as assigned

Required Education: Bachelor's degree.

Preferred Education: Master's Level Degree

Preferred Certification:

TOGAF 9 Certified

Microsoft Certified: Azure Solutions Architect Expert, or equivalent

EPIC Cogito, Caboodle Certification, Cognitive Computing Certification, or other certifications

Certified Kubernetes Administrator (CKA)

Required Experience: Ten years of experience in platform technology to include five years of experience in software integration, to include five years of supervisory/managerial experience. May substitute required education degree with additional years of equivalent experience on a one to one basis. Successful completion of the LEADing Self Accelerate program may substitute for one year of required supervisory or management experience.

Preferred Experience/Skills: Demonstrated expertise architecting complex hybrid cloud/on-prem infrastructures, AI/ML development and deployment ecosystems, Health IT integration, and governance frameworks. Proven ability to collaborate with senior leadership, clinicians, and technical teams to translate complex requirements into scalable platform architectures.

Work Location: Remote within Texas only.

It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law.