Location: Chicago, Illinois
Business Unit: Rush Medical Center
Hospital: Rush University Medical Center
Department: D&IS Innovation
Work Type: Full Time (Total FTE between 0.9 and 1.0)
Shift: Shift 1Work Schedule: 8 Hr (8:00:00 AM - 5:00:00 PM)
Pay Range: $65.44 - $98.81 per hour
Rush salaries are determined by many factors including, but not limited to, education, job-related experience and skills, as well as internal equity and industry specific market data. The pay range for each role reflects Rush's anticipated wage or salary reasonably expected to be offered for the position. Offers may vary depending on the circumstances of each case.
Summary:The Enterprise AI Architect is responsible for designing, governing, and enabling scalable, secure, and compliant artificial intelligence (AI), machine learning (ML), and analytics architectures across the healthcare enterprise. This role defines technical standards, integration patterns, and architectural guardrails for AI solutions, ensuring alignment with clinical workflows, enterprise platforms, data strategy, cybersecurity, and regulatory requirements.
- The Enterprise AI Architect serves as a senior technical authority bridging data engineering, AI/ML, infrastructure, applications, and cybersecurity, and supports teams from initial design through production operations.
- This is a full-time role reporting to the Director of AI & Innovation and working closely with AI, Data, Security, Clinical Informatics, and Operations teams. The role is a core member of the Rush AI Center of Excellence.
- Location: Remote or Hybrid. Periodic travel to Chicago is required for team and enterprise events.
Responsibilities:AI Architecture & Technical Strategy
- Define end-to-end architectures for AI solutions, from data ingestion through model deployment, monitoring, and retirement.
- Establish and maintain enterprise AI reference architectures, design patterns, and architectural standards.
- Ensure alignment with enterprise IT, cloud, data, integration, and application strategies.
- Evaluate emerging AI technologies, platforms, and tools for healthcare applicability and enterprise readiness.
Data, Platform & Integration Architecture
- Design scalable data architectures supporting AI/ML, analytics, and real-time decision support.
- Architect integrations across EHRs, enterprise systems, data platforms, and external services.
- Support healthcare interoperability standards including HL7, FHIR, APIs, and SMART on FHIR where applicable.
- Ensure data lineage, quality, reliability, and observability across AI pipelines.
AI/ML Enablement & Lifecycle Support
- Define architectural approaches for model development, deployment, versioning, monitoring, and retraining.
- Support MLOps practices including CI/CD, environment separation, automation, and reproducibility.
- Ensure architectures support explainability, auditability, performance monitoring, and model validation.
- Partner with data scientists and engineers to translate business and clinical use cases into technical designs.
Security, Privacy, Compliance & Responsible AI
- Embed security-by-design principles into AI architectures including IAM, network controls, encryption, and secrets management.
- Ensure compliance with HIPAA, data privacy, cybersecurity, and healthcare regulatory requirements.
- Design for PHI protection including minimum necessary access, de-identification and pseudonymization patterns, and auditable access controls.
- Support responsible AI practices including bias mitigation, transparency, explainability, and governance controls.
- Collaborate with cybersecurity, risk, compliance, and legal teams on architecture reviews and third-party/vendor AI risk assessments.
- Partner with clinical and operational stakeholders to ensure patient safety, clinical appropriateness, change management, and fallback behavior for AI-enabled workflows.
Governance & Technical Oversight
- Participate in AI governance bodies, architecture review boards, and technical decision forums.
- Provide technical guidance and approvals for AI solutions moving from pilot to production.
- Create and maintain architectural documentation including reference architectures, standards, decision records, threat models, and data flow diagrams.
- Identify and manage architectural risks, dependencies, and technical debt.
Clarification of Architectural Role
- This role includes both strategic and hands-on responsibilities:
- Define standards and guardrails while also contributing to reference implementations.
- Review and approve designs while partnering directly with delivery teams.
- Maintain ownership of enterprise AI architecture decisions while advising product and project teams.
- Engage at a design and code-adjacent level (Python/SQL, pipelines, cloud services) as needed.
Required Job Qualifications:- Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent experience.
- 8+ years of experience in IT, data, or solution architecture roles.
- 3+ years designing or supporting AI/ML platforms or advanced analytics solutions.
- Hands-on experience designing enterprise-scale data and AI architectures.
- Experience operating in complex, regulated environments such as healthcare.
- Strong relationship-building skills and a collaborative, outcomes-focused mindset.
Preferred Job Qualifications- Experience architecting AI/ML solutions in healthcare or life sciences.
- Familiarity with EHR platforms (e.g., Epic) and healthcare data models.
- Experience with cloud platforms, data lakes/warehouses, and MLOps tooling.
- Knowledge of AI governance, responsible AI frameworks, and model risk management.
- Architecture certifications or advanced technical credentials.
