Director, Artificial Intelligence

AEG Vision•Dallas, TX

16h

About The Position

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 Vision Opportunity to define AI strategy from the ground up in a rapidly scaling healthcare organization Real-world impact on patient care, clinician experience, and operational efficiency Executive visibility and influence Balance of innovation, responsibility, and practical execution

Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, or related field required
5+ years of experience in software engineering, data science, or AI/ML roles
Demonstrated experience building and deploying AI systems in production
Strong programming background (Python required; SQL required)
Hands-on experience with: Machine learning frameworks (TensorFlow, PyTorch, scikit-learn) Data engineering tools and pipelines Cloud AI/ML services
Experience designing AI architectures that integrate with enterprise systems
Working knowledge of: APIs and microservices Data security and privacy MLOps and model lifecycle management
Hands-on builder mindset with architectural depth
Strong communicator able to explain AI concepts to non-technical audiences
Pragmatic, ROI-focused approach to AI adoption
Comfortable operating in a fast-growing, multi-site healthcare environment
High integrity and respect for patient data and clinical workflows

Nice To Haves

Master's degree or PhD preferred but not required with equivalent experience
Experience in healthcare, health tech, SaaS, or highly regulated environments preferred
Experience with LLMs, generative AI, and retrieval-augmented generation (RAG)
Experience with optimization, forecasting, or scheduling algorithms
Familiarity with medical imaging, clinical data, or EHR integrations
Experience evaluating and managing AI vendors vs in-house build decisions

Responsibilities

Enhance analytics through deep AI integration with the enterprise data warehouse
Design and implement AI-enabled analytics solutions that sit natively on top of the enterprise data warehouse
Partner with Data Engineering and BI teams to: Embed machine learning, predictive analytics, and advanced forecasting directly into reporting and decision workflows Enable self-service and augmented analytics for business users
Eliminate manual effort across complex back-office workflows through AI-enabled automation
Identify high-friction, labor-intensive back-office processes suitable for AI-driven automation, including: Revenue cycle and accounting workflows Scheduling, capacity management, and exception handling Data reconciliation, validation, and anomaly detection Operational reporting and administrative processes
Translate 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 vendors
Model Development & Implementation
Personally contribute to: Prototyping AI/ML models Model selection and evaluation Prompt engineering and orchestration for LLM-based systems
Establish best practices for MLOps, model monitoring, versioning, and retraining.
Cross-Functional Collaboration
Partner closely with: Field Ops, Marketing, RCM, Accounting and Eyecare Operations IT, Infrastructure, and Security teams Product, Data, and Engineering teams
Act as a translator between business and technical stakeholders.
Guide responsible AI usage, governance, and compliance in healthcare settings.
Governance, Ethics & Compliance
Ensure AI solutions adhere to: HIPAA and healthcare data privacy requirements Security and access control best practices Ethical AI principles and explainability where required
Define policies for data usage, model validation, and risk management.