AI Architect - Hybrid in Charlotte, NC

We are currently seeking a AI Architect - Hybrid in Charlotte, NC to join our team in Charlotte, North Carolina (US-NC), United States (US).

Experience: 10+ years in technology / software architecture | 5+ years AI/ML | 3+ years Generative AI
Focus: Generative AI | LLMs | RAG | AI Agents | Cloud AI | Enterprise Architecture
Industry: Financial Services / Regulated Industries preferred

We are seeking an experienced AI Architect to design and lead the development of enterprise-scale Artificial Intelligence and Generative AI solutions
.

This role will define AI architecture, technology strategy, and implementation patterns while helping organizations move AI initiatives from proof of concept to secure, scalable production solutions
.

The ideal candidate combines strong enterprise architecture experience with hands‑on knowledge of LLMs, Generative AI, RAG, AI agents, cloud platforms, AI/ML services, and enterprise integration
. You will work closely with business leaders, data scientists, ML engineers, software developers, security teams, and enterprise architects to turn emerging AI capabilities into practical business solutions.

• Design and lead AI and Generative AI architectures for enterprise applications and business solutions.
• Define AI technology strategies, reference architectures, standards, and implementation roadmaps.
• Translate business requirements into secure, scalable, and production-ready AI solutions.
• Evaluate and recommend AI models, platforms, frameworks, and cloud services based on business and technical requirements.
• Establish architecture patterns that support scalability, reliability, performance, security, and cost efficiency.
• Guide AI initiatives from initial concept and PoC through production implementation.

• Design solutions leveraging LLMs, Generative AI, RAG, prompt engineering, AI agents, and AI/ML services
  .
• Architect retrieval-augmented generation (RAG) solutions, including:
• Vector search
• Embeddings
• Knowledge retrieval
• Knowledge bases
• Context management
• Evaluate LLM performance, accuracy, scalability, latency, and cost.
• Design approaches for prompt engineering, model selection, evaluation, and continuous improvement.
• Identify opportunities to apply AI and automation to complex enterprise business processes.
• Design secure and scalable AI solutions across AWS, Azure, and/or Google Cloud Platform (GCP).
• Integrate AI capabilities with enterprise applications, APIs, databases, data platforms, and distributed systems.
• Design REST API and service‑integration patterns for AI/LLM capabilities.
• Evaluate cloud‑native AI/ML services and determine the appropriate architecture for enterprise use cases.
• Ensure AI solutions integrate effectively with existing enterprise technology ecosystems.

• Establish architecture standards for AI security, governance, privacy, and responsible AI
  .
• Incorporate data protection, access controls, security, and compliance requirements into AI architectures.
• Support model‑risk management and appropriate governance for enterprise AI applications.
• Define controls for AI solution monitoring, evaluation, auditability, and operational risk.
• Partner with security, risk, privacy, and compliance teams to ensure solutions meet enterprise and regulatory requirements.
• Lead AI proofs of concept, prototypes, and technical evaluations.
• Assess emerging AI technologies and determine their practical enterprise applications.
• Establish criteria for moving AI solutions from experimentation to production.
• Define requirements for performance, scalability, reliability, monitoring, observability, and cost management.
• Help establish repeatable architecture patterns and best practices for enterprise…