Location: Location: Charlotte, NC / NY/ Atlanta, GA/Chicago, IL Role Overview: We are seeking a highly experienced Agentic AI Architect to lead the design and implementation of enterprise-scale Agentic AI solutions. This role will be responsible for defining architecture, guiding development teams, and ensuring secure, scalable, and high-performing AI systems that leverage Large Language Models (LLMs), multi-agent frameworks, and enterprise integrations. The ideal candidate combines deep expertise in AI/ML, software architecture, and enterprise solution design, with a proven track record of delivering complex, production-grade AI systems. Job Description: AI Solution Architecture • Define end-to-end architecture for enterprise Agentic AI solutions. • Design multi-agent systems, orchestration layers, and enterprise integrations. • Establish architectural standards, design patterns, and best practices. Agent Framework Development • Design and implement scalable agent frameworks using technologies such as: • LangChain • Semantic Kernel • AutoGen • CrewAI • Other emerging agent orchestration frameworks • Build robust and maintainable AI architectures capable of enterprise-scale deployment. LLM & AI Engineering • Lead the development of LLM-driven solutions using: • Azure OpenAI • OpenAI • Anthropic • Similar foundation model platforms • Develop effective prompting strategies and optimize model interactions. • Design solutions that maximize accuracy, performance, and reliability. RAG & Knowledge Systems • Architect and implement: • Retrieval-Augmented Generation (RAG) pipelines • Knowledge retrieval architectures • Memory management systems • Tool integration frameworks • Enable AI agents to effectively access and utilize enterprise knowledge. Governance, Security & Responsible AI • Establish AI governance frameworks and standards. • Implement Responsible AI principles and controls. • Ensure compliance with security, privacy, and data governance requirements. • Define risk management practices for enterprise AI solutions. Enterprise Integration • Design integration strategies with enterprise applications and platforms, including: • CRM systems • ERP platforms • APIs • Data platforms • Third-party services • Develop scalable and secure enterprise integration patterns. Technical Leadership • Provide technical leadership and mentorship to engineering teams. • Guide architecture decisions and implementation approaches. • Review code quality, scalability, and overall solution design. • Drive engineering excellence and innovation across teams. Stakeholder Collaboration • Partner with business and technology stakeholders to translate business requirements into technical solutions. • Communicate architectural strategies and implementation roadmaps to executive leadership. Performance & Innovation • Ensure AI systems meet targets for: • Performance • Scalability • Reliability • Cost optimization • Continuously evaluate emerging AI technologies, tools, and frameworks to drive innovation.