Enterprise AI Architect Charlotte, NC (Hybrid, 3 days onsite, 2 days offsite)

Role: Enterprise AI Architect

Location: Charlotte, NC (Hybrid, 3 days onsite, 2 days offsite)

Duration: CTH after 12 months

Responsibilities:
Enterprise AI Architecture:

• Design and maintain Client's enterprise AI platform architecture
• Establish technical standards for AI solution implementation
• Define integration patterns for AI services across Client's technology landscape
• Create and maintain AI reference architectures, including RAG implementations
• Develop model registry and lifecycle management frameworks

Technical Leadership:

• Lead the technical design of AI platform components and services
• Define agent orchestration standards and patterns
• Establish guardrails for AI implementation and deployment
• Create architectural patterns for knowledge base governance
• Guide teams on technical implementation of AI solutions

Standards & Governance:

• Develop technical standards for AI model deployment and management
• Create governance frameworks for AI platform components
• Define security and compliance requirements for AI implementations
• Establish monitoring and observability standards
• Maintain architectural compliance across AI initiatives

Solution Design & Review:

• Review and approve AI solution designs
• Ensure alignment with enterprise architecture standards
• Identify opportunities for reusable components
• Guide teams on technical implementation approaches
• Evaluate new AI technologies and platforms

Platform Evolution:

• Drive the evolution of Client's AI platform capabilities
• Identify and evaluate emerging AI technologies
• Define roadmap for platform capabilities and features
• Ensure scalability and performance of AI solutions
• Guide integration with existing enterprise systems

Requirements:

• 10+ years of enterprise architecture experience
• 5+ years specifically focused on AI/ML platforms and solutions
• Strong background in large-scale distributed systems
• Experience with major cloud AI services and platforms
• Deep understanding of AI security and governance requirements
• Expert knowledge of AI/ML architectures and platforms
• Strong enterprise architecture capabilities
• Deep understanding of security and compliance requirements
• Experience with cloud-native architectures
• Knowledge of AI model lifecycle management
• Expertise in data architecture and integration patterns
• Strong technical leadership and communication abilities
• Large Language Models and GenAI technologies
• RAG architectures and implementation patterns
• Cloud AI services (AWS, Azure, Google Cloud Platform)
• Enterprise integration patterns
• Security and compliance frameworks
• Data architecture and governance
• Model operations and MLOps