AI Architect

Kforce's client is seeking an AI Architect in Cincinnati, OH. Overview: The client is looking for an AI Architect - Agentic Platforms to define and lead the architecture for their firm's AI agent ecosystem, enabling intelligent, grounded, and reliable AI systems that operate across millions of interactions every day. This is a high-impact, enterprise architecture leadership role focused on platform strategy, technical standards, and cross-organization influence. This role is responsible for designing and governing the architecture for agent-based integrations, agent registries, scoring/evals infrastructure, grounding patterns, and multi-agent orchestration platforms. The AI Architect provides deep technical leadership across engineering, product, data science, security, and cloud teams to ensure that agents are built safely, consistently, and with enterprise-grade reliability, performance, and observability. This role combines expertise in large-scale AI systems, distributed cloud architecture, and modern agentic frameworks. Duties Include: Retrieval-Augmented Generation (RAG):

• Architect and develop systems that connect agents to trusted enterprise data, APIs, and business services
• Develop architectural patterns for safe, governed agent execution aligned with Responsible AI principles
• Architect scalable, fault-tolerant AI agent platforms across hybrid cloud environments (Azure & GCP)
• Establish architecture standards ensuring low latency, high availability, resiliency, and observability
• Partner with cloud and platform engineering teams to deliver containerized, API-driven, secure infrastructure for agent workloads
• Define platform lifecycle patterns including versioning, release gating, rollback strategies, and performance benchmarking
• Enable cost-efficient scaling of AI workloads across millions of enterprises and customer interactions
• Designing enterprise AI/ML systems; 1+ years of hands-on with GenAI, agentic workflows, RAG, LLM-based integrations, or multi-agent systems
• Experience in cloud and distributed systems architecture focused on scalability, reliability, observability, and performance
• Strong expertise with agentic frameworks and tooling (MCP, LangChain, LangGraph, LlamaIndex, autogen, crewai, Agent sdk, OpenAI SDK, etc.)
• Hands-on experience in modern software development and engineering practices
• Proven experience integrating APIs and enterprise systems into agentic platforms and workflows
• Ability to rapidly build AI-driven prototypes, proofs of concept, and demo-ready product experiences
• Experience defining and governing enterprise architecture standards, patterns, and reference architectures
• Deep understanding of MCP servers, tool calling, registries, eval pipelines, agent observability, and multi-agent orchestration
• Hands-on experience with Azure and GCP, including Kubernetes, containerization, identity, networking, CI/CD, and API platforms
• Familiarity with AIOps/MLOps stacks (MLflow, model registries, vector DBs, semantic layers, feature stores, monitoring)
• Strong knowledge of security, compliance, risk, and Responsible AI (RAI) considerations for enterprise agent systems
• Demonstrated ability to partner across engineering, data science, product, and security teams to deliver complex AI platform architectures