Director, Artificial Intelligence

AEG Vision is seeking a Director of Artificial Intelligence to lead the architecture, and hands-on implementation of AI solutions across the enterprise. This role is both strategic and deeply technical--ideal for a leader who can design AI architecture, build and deploy models, and guide teams, not just manage vendors or research.The Director of AI will define how AI is used across automation, patient engagement, scheduling, revenue optimization, imaging, clinical operations, and internal systems, while ensuring solutions are scalable, secure, compliant, and practical for real-world healthcare environments.Why Join AEG VisionOpportunity to define AI strategy from the ground up in a rapidly scaling healthcare organizationReal-world impact on patient care, clinician experience, and operational efficiencyExecutive visibility and influenceBalance of innovation, responsibility, and practical executionKey ResponsibilitiesEnhance analytics through deep AI integration with the enterprise data warehouseDesign and implement AI-enabled analytics solutions that sit natively on top of the enterprise data warehousePartner with Data Engineering and BI teams to:Embed machine learning, predictive analytics, and advanced forecasting directly into reporting and decision workflowsEnable self-service and augmented analytics for business usersEliminate manual effort across complex back-office workflows through AI-enabled automationIdentify high-friction, labor-intensive back-office processes suitable for AI-driven automation, including:Revenue cycle and accounting workflowsScheduling, capacity management, and exception handlingData reconciliation, validation, and anomaly detectionOperational reporting and administrative processesTranslate business problems into AI-driven solutions with measurable ROI.Continuously evaluate, pilot, and govern external AI platforms and vendors: Own the ongoing evaluation and governance of external AI tools, platforms, and vendorsModel Development & ImplementationPersonally contribute to:Prototyping AI/ML modelsModel selection and evaluationPrompt engineering and orchestration for LLM-based systemsEstablish best practices for MLOps, model monitoring, versioning, and retraining.Cross-Functional CollaborationPartner closely with:Field Ops, Marketing, RCM, Accounting and Eyecare OperationsIT, Infrastructure, and Security teamsProduct, Data, and Engineering teamsAct as a translator between business and technical stakeholders.Guide responsible AI usage, governance, and compliance in healthcare settings.Governance, Ethics & ComplianceEnsure AI solutions adhere to:HIPAA and healthcare data privacy requirementsSecurity and access control best practicesEthical AI principles and explainability where requiredDefine policies for data usage, model validation, and risk management.EducationBachelor's degree in Computer Science, Engineering, Data Science, or related field requiredMaster's degree or PhD preferred but not required with equivalent experienceExperience5+ years of experience in software engineering, data science, or AI/ML rolesDemonstrated experience building and deploying AI systems in productionExperience in healthcare, health tech, SaaS, or highly regulated environments preferredTechnical Skills (Must Have)Strong programming background (Python required; SQL required)Hands-on experience with:Machine learning frameworks (TensorFlow, PyTorch, scikit-learn)Data engineering tools and pipelinesCloud AI/ML servicesExperience designing AI architectures that integrate with enterprise systemsWorking knowledge of:APIs and microservicesData security and privacyMLOps and model lifecycle managementBonus SkillsExperience with LLMs, generative AI, and retrieval-augmented generation (RAG)Experience with optimization, forecasting, or scheduling algorithmsFamiliarity with medical imaging, clinical data, or EHR integrationsExperience evaluating and managing AI vendors vs in-house build decisionsLeadership & Personal AttributesHands-on builder mindset with architectural depthStrong communicator able to explain AI concepts to non-technical audiencesPragmatic, ROI-focused approach to AI adoptionComfortable operating in a fast-growing, multi-site healthcare environmentHigh integrity and respect for patient data and clinical workflows

Created: 2026-04-02