AI Solutions Architect

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)