Location: Chicago, Illinois\n \nBusiness Unit: Rush Medical Center\n \nHospital: Rush University Medical Center\n \nDepartment: D&IS Innovation\n \nWork Type: Full Time (Total FTE between 0.9 and 1.0)\n \nShift: Shift 1\n \nWork Schedule: 8 Hr (8:00:00 AM - 5:00:00 PM)\n \nRush offers exceptional rewards and benefits learn more at our Rush benefits page (https://www.rush.edu/rush-careers/employee-benefits).\n \nPay Range: $41.88 - $70.36 per hour\n \nRush 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.\n \nSummary:\n \nThe 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.\n \nThe 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.\n \nThis 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.\n \nLocation: Remote or Hybrid. Periodic travel to Chicago is required for team and enterprise events.\n \nOther information:\n \nRequired Job Qualifications:\n \nBachelor's degree in Computer Science, Engineering, Information Systems, or equivalent experience.\n \n8+ years of experience in IT, data, or solution architecture roles.\n \n3+ years designing or supporting AI/ML platforms or advanced analytics solutions.\n \nHands-on experience designing enterprise-scale data and AI architectures.\n \nExperience operating in complex, regulated environments such as healthcare.\n \nStrong relationship-building skills and a collaborative, outcomes-focused mindset.\n \nPreferred Job Qualifications:\n \nExperience architecting AI/ML solutions in healthcare or life sciences.\n \nFamiliarity with EHR platforms (e.g., Epic) and healthcare data models.\n \nExperience with cloud platforms, data lakes/warehouses, and MLOps tooling.\n \nKnowledge of AI governance, responsible AI frameworks, and model risk management.\n \nArchitecture certifications or advanced technical credentials.\n \nPhysical Demands:\n \nThis is a computer/desk based job with occasional onsite visits needed\n \nCompetencies: \n \nEnterprise and solution architecture leadership\n \nDeep AI and analytics technical expertise\n \nSystems thinking and integration design\n \nRisk-aware and compliance-driven decision making\n \nClear and effective technical communication\n \nAbility to balance innovation, scalability, security, and regulatory requirements\n \nStrategic yet hands-on when needed\n \nDetail-oriented with strong architectural discipline\n \nComfortable influencing without direct authority\n \nOutcome-focused and accountable\n \nThrives in fast-evolving AI environments\n \nPositive attitude and willingness to do what it takes to get the job done right\n \nAbility to create strong working relationships with business partners across the organization\n \nDisclaimer:\n \nThe above is intended to describe the general content of and requirements for the performance of this job. It is not to be construed as an exhaustive statement of duties, responsibilities or requirements.\n \nResponsibilities:\n \nAI Architecture & Technical Strategy\n \nDefine end-to-end architectures for AI solutions, from data ingestion through model deployment, monitoring, and retirement.\n \nEstablish and maintain enterprise AI reference architectures, design patterns, and architectural standards.\n \nEnsure alignment with enterprise IT, cloud, data, integration, and application strategies.\n \nEvaluate emerging AI technologies, platforms, and tools for healthcare applicability and enterprise readiness.\n \nData, Platform & Integration Architecture\n \nDesign scalable data architectures supporting AI/ML, analytics, and real-time decision support.\n \nArchitect integrations across EHRs, enterprise systems, data platforms, and external services.\n \nSupport healthcare interoperability standards including HL7, FHIR, APIs, and SMART on FHIR where applicable.\n \nEnsure data lineage, quality, reliability, and observability across AI pipelines.\n \nAI/ML Enablement & Lifecycle Support\n \nDefine architectural approaches for model development, deployment, versioning, monitoring, and retraining.\n \nSupport MLOps practices including CI/CD, environment separation, automation, and reproducibility.\n \nEnsure architectures support explainability, auditability, performance monitoring, and model validation.\n \nPartner with data scientists and engineers to translate business and clinical use cases into technical designs.\n \nSecurity, Privacy, Compliance & Responsible AI\n \nEmbed security-by-design principles into AI architectures including IAM, network controls, encryption, and secrets management.\n \nEnsure compliance with HIPAA, data privacy, cybersecurity, and healthcare regulatory requirements.\n \nDesign for PHI protection including minimum necessary access, de-identification and pseudonymization patterns, and auditable access controls.\n \nSupport responsible AI practices including bias mitigation, transparency, explainability, and governance controls.\n \nCollaborate with cybersecurity, risk, compliance, and legal teams on architecture reviews and third-party/vendor AI risk assessments.\n \nPartner with clinical and operational stakeholders to ensure patient safety, clinical appropriateness, change management, and fallback behavior for AI-enabled workflows.\n \nGovernance & Technical Oversight\n \nParticipate in AI governance bodies, architecture review boards, and technical decision forums.\n \nProvide technical guidance and approvals for AI solutions moving from pilot to production.\n \nCreate and maintain architectural documentation including reference architectures, standards, decision records, threat models, and data flow diagrams.\n \nIdentify and manage architectural risks, dependencies, and technical debt.\n \nClarification of Architectural Role\n \nThis role includes both strategic and hands-on responsibilities:\n \nDefine standards and guardrails while also contributing to reference implementations.\n \nReview and approve designs while partnering directly with delivery teams.\n \nMaintain ownership of enterprise AI architecture decisions while advising product and project teams.\n \nEngage at a design and code-adjacent level (Python/SQL, pipelines, cloud services) as needed.\n \nRush is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other legally protected characteristics.\n\n