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.