Principal AI Architect
Agentic AI, MCP, Azure/GCP | Consumer Packaged Goods
Location: Cincinnati, Ohio, USA
Employment: Full-Time / Permanent
Compensation: Approximately $200,000 annual base salary
Seniority: Principal / Senior Enterprise Architect
Industry: Consumer Packaged Goods (CPG)
Priority: Urgent / Critical
POSITION OVERVIEW
We are seeking an accomplished Principal AI Architect to lead the design and implementation of a Foundational AI Platform supporting enterprise-scale AI transformation initiatives.
This highly strategic and technically demanding role combines agentic AI architecture, MCP server and gateway implementation, enterprise AI governance, observability, and cloud-native AI solutions across Microsoft Azure and Google Cloud Platform.
The successful candidate will collaborate with enterprise AI platform teams, knowledge engineering specialists, business stakeholders, and technology partners to establish scalable, secure, and production-ready AI architectures.
This is a hands-on enterprise architecture leadership role, not a conventional AI development or prompt engineering position.
KEY RESPONSIBILITIES
1. Foundational AI Platform Architecture
• Own the end-to-end architecture of enterprise agentic AI platforms.
• Design multi-agent orchestration, planning and reasoning workflows, agent communication, fallback mechanisms, and human-in-the-loop controls.
• Establish agent management and control-plane capabilities, including registries, versioning, deployment, configuration, and governance.
• Design Responsible AI guardrails, security controls, PII protection, and policy enforcement.
• Develop observability and evaluation architectures covering tracing, performance, cost telemetry, regression testing, and quality monitoring.
• Create reusable AI agents, copilots, and implementation frameworks.
2. MCP Server and Gateway Architecture
• Define and implement MCP server reference architectures.
• Design and integrate enterprise MCP gateways.
• Establish tool discovery, registration, routing, authorization, and multi-tenant capabilities.
• Integrate enterprise APIs and services through MCP-compatible infrastructure.
• Implement secure identity and access management using OAuth, OIDC, and related enterprise security protocols.
Hands-on MCP server development and gateway integration experience is mandatory.
3. Enterprise Knowledge Integration
• Collaborate with knowledge engineering teams to integrate enterprise retrieval services.
• Define retrieval contracts, schemas, and integration requirements.
• Incorporate RAG and GraphRAG outputs into agentic applications.
• Establish quality feedback and evaluation mechanisms.
4. Azure and GCP AI Solutions
• Architect production-grade AI solutions across Microsoft Azure and Google Cloud Platform.
• Leverage Azure OpenAI, Azure AI Foundry, Azure AI Search, Cosmos DB, and AKS.
• Leverage Vertex AI, Gemini, BigQuery, and GKE.
• Design hybrid and cross-cloud AI architectures.
• Establish secure connectivity, identity propagation, model routing, and scalable infrastructure.
5. Engineering Leadership
• Lead enterprise architecture reviews and establish technical standards.
• Mentor AI engineering teams and technical delivery groups.
• Collaborate with stakeholders on architecture strategy and platform roadmaps.
• Promote AI-native software development practices.
• Support scalable, secure, and reliable enterprise implementation.
