Job description

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 RxNorm 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 related technologies helping high-performance AI workloads Proven experience architecting and integrating AI services across multi-cloud environments, including Microsoft Azure and Google Cloud Platform (GCP) Extensive expertise implementing AI observability frameworks, telemetry standards, distributed tracing, evaluation methodologies, production monitoring, and reliability engineering practices Experience building AI safety frameworks leveraging adversarial testing, LLM-as-a-judge methodologies, evaluation harnesses, and governance controls Ability to establish technical standards, influence executive decision-making, mentor senior engineering teams, and drive enterprise-wide architectural alignment Exceptional communication and collaborator management skills with demonstrated ability to bridge executive strategy and engaged engineering execution BENEFITS Our competitive benefits package includes the following • Immediate eligibility for health and welfare benefits • 401(k) savings plan with dollar-for-dollar match up to 5% • Tuition Reimbursement • PTO accrual beginning Day 1 Note: Benefits may vary based upon position type and/or level QUALIFICATIONS • EDUCATION - Bachelor's or 4 years of work experience above the minimum qualification

Requirements

Must-haves

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
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 RxNorm
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 related technologies helping high-performance AI workloads
Proven experience architecting and integrating AI services across multi-cloud environments, including Microsoft Azure and Google Cloud Platform (GCP)
Extensive expertise implementing AI observability frameworks, telemetry standards, distributed tracing, evaluation methodologies, production monitoring, and reliability engineering practices

Nice-to-haves

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aiazuremachine learninggcpllm

Platform: phenom
Effort: medium

Benefits

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Vision

401(K)

Pto