Agentic AI Architect (Charlotte)

Role : Agentic AI Architect

Duration : Long Term Contract

Location : Charlotte NC Hybrid

We are looking for 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 scalable, secure, and high-performing AI systems that leverage LLMs, multi-agent frameworks, and enterprise integrations.

The ideal candidate combines deep technical expertise in AI/ML with strong architectural design skills and experience delivering complex, production-grade AI solutions.

Key Responsibilities

Define end-to-end architecture for agentic AI solutions, including multi-agent systems, orchestration layers, and integrations

Design and implement scalable agent frameworks using technologies such as LangChain, Semantic Kernel, AutoGen, CrewAI, etc.

Lead development of LLM-driven systems (Azure OpenAI, OpenAI, Anthropic, etc.) with robust prompting strategies

Architect RAG pipelines, memory systems, and tool integration layers

Establish best practices for AI governance, security, and Responsible AI

Design integration strategies with enterprise systems (CRM, ERP, APIs, data platforms)

Provide technical leadership, mentorship, and code quality oversight to development teams

Collaborate with business stakeholders to translate requirements into technical solutions

Ensure performance, scalability, reliability, and cost optimization of AI systems

Drive innovation by evaluating emerging AI technologies and frameworks

Required Skills & Experience

10+ years of experience in software engineering, AI/ML, or solution architecture

Strong expertise in Python and modern backend architectures

Hands-on experience with LLM ecosystems and agent frameworks

Proven experience designing multi-agent systems and distributed architectures

Deep understanding of:

Prompt engineering and LLM optimization

Embeddings and vector databases (FAISS, Pinecone, Azure AI Search, etc.)

Retrieval-Augmented Generation (RAG)

Experience with cloud platforms (Azure preferred, AWS/GCP acceptable)

Strong knowledge of API design, microservices, and event-driven architecture

Familiarity with security, authentication (OAuth, SSO), and data governance

Experience leading technical teams and driving architecture decisions

Good to Have

Experience with Model Context Protocol (MCP) or similar integration standards

Exposure to enterprise AI transformation programs

Knowledge of MLOps / LLMOps frameworks

Experience with frontend or conversational UI frameworks

Certifications (Azure AI Engineer, Solutions Architect, etc.)