What are the responsibilities and job description for the AI Architect position at PRI Technology?
This is a hybrid role in Charlotte, NC
Local candidates only
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, GCP)
- Enterprise integration patterns
- Security and compliance frameworks
- Data architecture and governance
- Model operations and MLOps