AI Technical Architect

Job Description

Location: Chicago, Illinois / Remote

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 1

Work Schedule: 8 Hr (8:00:00 AM - 5:00:00 PM)

Rush offers exceptional rewards and benefits learn more at our Rush benefits page (https://www.rush.edu/rush-careers/employee-benefits).

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:

Enterprise AI Architect | Build the Future of AI in Healthcare

Rush is seeking an Enterprise AI Architect to shape the strategy, architecture, and responsible implementation of artificial intelligence across our healthcare enterprise. As a core member of the Rush AI Center of Excellence, you'll define the technical standards, integration patterns, and architectural frameworks that enable secure, scalable, and compliant AI, machine learning, and advanced analytics solutions.

Reporting to the Director of AI & Innovation, you'll partner with AI, data engineering, cybersecurity, clinical informatics, and operational teams to translate clinical and business needs into enterprise-ready technical solutions. From evaluating emerging technologies and designing AI-enabled platforms to guiding solutions from pilot to production, you'll help ensure our AI capabilities are reliable, secure, and aligned with enterprise strategy.

You'll design end-to-end AI architectures spanning data pipelines, cloud platforms, enterprise integrations, model deployment, and ongoing performance monitoring. You'll also help establish MLOps practices, strengthen healthcare data interoperability, and embed security, privacy, regulatory compliance, and responsible AI principles into solution designs. This role combines strategic architectural leadership with hands-on technical engagement, including design reviews, reference implementations, and collaboration with delivery teams.

What you'll bring: A bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent experience; at least 8 years of experience in IT, data, or solution architecture; and at least 3 years designing or supporting AI/ML platforms or advanced analytics solutions. You'll also bring experience designing enterprise-scale data and AI architectures, ideally in a complex, regulated environment. Experience with healthcare AI, EHR platforms such as Epic, cloud data platforms, MLOps, and responsible AI governance is a plus.

Work arrangement: Remote or hybrid, with periodic travel to Chicago for team collaboration 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

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