AI Solutions Architect

Lumexa Imaging is one of the country's largest providers of outpatient medical imaging. With over 5,000 team members and more than 185 outpatient imaging centers across 13 states, our team conducts more than 4 million outpatient studies annually. We are the partner of choice for health systems and radiologists, delivering best-in-class clinical excellence, operations, and state-of-the-art technology across our platform.
AI Solutions Architect

Role Overview

Lumexa Imaging is seeking an experienced AI Solutions Architect to lead the design and delivery of AI-driven solutions across business and operational functions. This role will report to the SVP of AI Integrations and work closely with the AI and IT teams, as well as the enterprise-wide AI Governance Council to intake requests, define solution approaches, and ensure seamless integration of AI into enterprise workflows.

This role requires a blend of enterprise solution architecture and hands-on AI implementation capability, with the ability to both design scalable solutions and independently build or prototype AI-driven workflows where needed.

The ideal candidate combines strong business acumen, deep understanding of enterprise system architecture, and the ability to translate ambiguous problem statements into scalable, practical AI solutions. While the primary focus is on business and operational use cases, familiarity with radiology and imaging workflows is highly preferred.

This role operates across the full lifecycle of AI initiatives, from intake and scoping through solution design, integration, and stakeholder alignment, supporting a structured pipeline of enterprise AI opportunities.

Key Responsibilities

AI Solution Design & Architecture

Lead end-to-end solution design for AI use cases, from intake through implementation planning

Translate business problems into technical architectures, workflows, and system integration designs

Determine when to:

Leverage existing enterprise tools and platforms, vs.

Recommend new vendors, capabilities, or configuration changes

Define solution components including data flows, APIs, integrations, and user workflows

Hands-on AI Development & Prototyping

Independently design, prototype, and implement AI-enabled solutions, particularly for LLM-driven and workflow-based use cases

Rapidly develop MVPs and proof-of-concepts to validate solution feasibility and business value

Configure and leverage enterprise AI tools (e.g., copilots, automation platforms, embedded AI features) to deliver solutions

Progress solutions from prototype to scalable implementation in collaboration with engineering where needed

AI Project Intake & Scoping

Partner with stakeholders to clarify problem statements, desired outcomes, and success metrics

Conduct structured intake and scope feasibility, level of effort, and dependencies

Align proposed solutions with enterprise priorities, governance standards, and KPIs

Contribute to and help manage the AI project pipeline and prioritization process

Workflow Integration & Optimization

Design solutions that integrate seamlessly into existing operational and clinical-adjacent workflows

Map current-state vs. future-state workflows and identify efficiency gains and automation opportunities

Ensure solutions are usable, scalable, and aligned with end-user needs

Partner with IT and operations teams to support implementation and adoption

Stakeholder Management & Change Enablement

Serve as a key interface between business leaders, IT, clinical stakeholders, and vendors

Facilitate working sessions to gather requirements, validate designs, and drive alignment

Navigate a matrixed organization with competing priorities and stakeholders

Clearly communicate tradeoffs, risks, and recommendations

Vendor & Technology Evaluation

Evaluate AI tools, platforms, and vendors for fit, scalability, security, and ROI

Develop recommendations including build vs. buy decisions

Partner with governance, legal, and security teams to support vendor selection and risk review

Execution Support & Continuous Improvement

Collaborate with AI engineering and product teams to ensure effective execution of designed solutions

Monitor performance against defined KPIs and identify opportunities to improve outcomes

Contribute to evolving AI architecture standards, best practices, and playbooks

Required Qualifications

5+ years of experience in solution architecture, enterprise systems, or technology consulting

Proven experience designing and implementing cross-system workflows and integrations

Strong understanding of:

Enterprise systems (e.g., ERP, CRM, RCM, scheduling, contact center)

APIs, data integration, and system interoperability

Demonstrated ability to translate business needs into technical solutions

Ability to leverage and configure underlying technical components (e.g., APIs, data flows, orchestration tools, and data sources) to independently design and implement AI-driven solutions, including LLM-based applications (e.g., prompt-driven workflows, copilots, document processing, or conversational interfaces)

Strong understanding of how to apply AI appropriately within enterprise workflows, including awareness of limitations, tradeoffs, and risks

Strong stakeholder management and communication skills across technical and business audiences

Ability to operate independently in ambiguous, fast-moving environments

Excellent project scoping, prioritization, and execution skills, with the ability to manage multiple concurrent initiatives and drive alignment across cross-functional stakeholders with varying levels of technical and operational fluency

Demonstrated ability and strong desire to continuously learn and adapt in a rapidly evolving AI landscape, including proactively staying current on emerging tools, capabilities, and best practices and translating that knowledge into practical enterprise applications

Preferred Qualifications

Experience scaling AI solutions from prototype to enterprise deployment

Familiarity with healthcare operations and radiology workflows (PACS, RIS, scheduling, center operations) a strong plus

Strong understanding of healthcare regulations (e.g., HIPAA, FDA) and compliance requirements related to AI in healthcare

Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and tools relevant to AI and healthcare solutions

Experience in vendor evaluation, procurement, and solution selection

Exposure to AI governance, compliance, or data security considerations

Success Profile

Self-starter: Proactively identifies opportunities and drives work forward with minimal direction

Structured thinker: Brings clarity to ambiguous problems and defines actionable paths

Hands-on and pragmatic: Comfortable building and iterating on solutions directly to accelerate progress

Business-oriented: Focuses on practical, high-impact outcomes over theoretical solutions

Collaborative but decisive: Seeks input and effectively engages stakeholders while driving clarity

Orchestrator: Aligns diverse stakeholders and keeps parallel workstreams moving across a matrixed environment

Continuous learner: Maintains an open mindset and actively stays current on emerging AI capabilities, rapidly translating new developments into real-world use cases

Adaptable: Thrives in a fast-evolving AI and enterprise environment

Example Scope of Work

Automating back-office workflows (e.g., finance, RCM, HR, contact center)

Designing and deploying LLM-enabled workflow automation and decision support tools for operational efficiency

Integrating AI capabilities into existing enterprise systems

Supporting clinical-adjacent workflows (e.g., scheduling, reporting support, center operations)

Lumexa Imaging provides a competitive compensation program to attract, retain, and motivate a high-performance workforce.
 

Lumexa Imaging is an equal opportunity employer.