Principle AI Architect

Job quisition:
Company:
Baylor Scott & White Health

Location:

Dallas, Texas, United States

Employment type:

Full Time Category:
Technology Industry: Healthcare Level: Individual Contributor

Shift: Day Job Posted: JOB SUMMARY: The Ethic AI Architect serves as the enterprise technical mentor responsible for defining, governing, and advancing the AI architecture strategy across Baylor Scott & White Health. Provides experienced leadership bridging executive vision and engineering execution, establishing scalable, secure, observable, and composable AI systems that improve patient experiences, clinical outcomes, operational efficiency, and administrative effectiveness. Manages the development of enterprise AI reference architectures, agentic systems, clinical knowledge platforms, governance frameworks, and production AI standards while serving as the senior technical authority on AI architecture, interoperability, and platform strategy.

ESSENTIAL FUNCTIONS OF

THE ROLE Manager the enterprise AI architecture strategy, defining the technical framework for AI solutions across clinical, operational, administrative, and consumer domains.

Partner with leadership and engineering teams to turn AI strategy into scalable architectures, standards, and delivery frameworks.

Serve as a senior advisor on AI platform strategy, guiding technology selection, vendor evaluations, contract reviews, and integrations across Azure and Google cloud.

Establish enterprise reference architectures for agentic systems, model orchestration frameworks, clinical knowledge graphs, FHIR-based data products, and enterprise integration patterns

Manage the enterprise agentic AI platform strategy, defining standards for orchestration, agent engagement, memory management, and enterprise integration.

Design end-to-end AI solutions for patient and clinician experiences, ensuring effective workflows, secure data access, safety, and operational readiness.

Define standards for PHI access, security, auditability and the governed use of FHIR resources, knowledge graphs, and enterprise platforms.

Establish enterprise AI evaluation and safety frameworks, including testing methodologies, guardrails, release criteria, and AI model evaluation standards.

Define AI observability, reliability, and monitoring standards to ensure performance, safety, efficiency, and cost optimization. Establish and supervise the enterprise AI architecture practice, mentoring engineering teams, defining technical standards, and conducting architecture and design reviews

Partner closely with clinical informatics, security, compliance, data governance, operations, and executive leadership to ensure AI initiatives align with organizational targets and regulatory requirements KEY SUCCESS FACTORS
10+ years of experience in applied artificial intelligence, advanced data architecture, software engineering, and enterprise platform design

Demonstrated success serving in highly senior individual contributor leadership roles such as Chief Engineer, Principal Architect, Distinguished Engineer, or equivalent technical leadership positions

Deep healthcare industry experience within complex regulatory environments, including HIPAA, HB 300, PHI governance, and large payer or provider organizations

Extensive expertise designing, deploying, and operating production-scale machine learning, generative AI, and large language model solutions

Deep expertise building and orchestrating agentic AI systems utilizing modern orchestration frameworks and interoperability standards, including ADK, A2A, MCP, or equivalent technologies

Advanced experience designing enterprise AI platforms, multi-agent architectures, orchestration frameworks, memory systems, tool integrations, and autonomous workflow environments

Extensive experience architecting clinical data platforms utilizing FHIR resources, healthcare interoperability standards, and enterprise clinical data products

Expertise designing and implementing clinical knowledge graphs utilizing graph database technologies such as Neo4j or comparable platforms

Working knowledge of healthcare interoperability standards and clinical terminologies, including HL7v2, X12, ICD-10, CPT/HCPCS, SNOMED CT, LOINC, and Rx Norm Proven  knowledge of terminology normalization, semantic mapping, retrieval architectures, grounding strategies, and AI safety methods within healthcare environments

Deep working knowledge of modern data architecture platforms and engineering ecosystems, including Snowflake, dbt, DuckDB, and…