Location: Cincinnati, OH
Salary: $85.00 USD Hourly - $90.00 USD Hourly
Description: The AI Enablement team is seeking an AI Architect, Agentic Platforms to define and build the architectural foundations for an enterprise-wide agent ecosystem. This role focuses on designing and governing the architecture for agent-based integrations, agent registries, evaluation and scoring pipelines, grounding patterns, and multi-agent orchestration frameworks.
You will provide deep technical leadership across engineering, product, data science, security, and cloud organizations-ensuring agents are developed with enterprise-grade reliability, safety, performance, and observability. This role requires strong expertise in large-scale AI systems, distributed cloud architecture, and modern agentic frameworks.
Minimum Qualifications - 10+ years in cloud and distributed systems architecture with a focus on scalability, reliability, performance, and observability.
- 7+ years designing enterprise AI/ML systems.
- 1+ year hands-on with GenAI, agentic workflows, RAG pipelines, LLM integrations, or multi-agent systems.
- Strong experience with modern agentic frameworks and tooling (e.g., MCP, LangChain, LangGraph, LlamaIndex, Autogen, CrewAI, Agent SDK, OpenAI SDK, etc.).
- Solid software engineering foundation with hands-on development experience.
- Proven experience integrating APIs and enterprise systems into agentic workflows.
- Ability to rapidly build AI prototypes, PoCs, and demo-ready experiences.
Preferred Qualifications - Experience defining and governing enterprise architecture standards, patterns, and reference architectures.
- Deep understanding of MCP servers, tool calling, registries, evaluation pipelines, observability, and multi-agent orchestration.
- Hands-on experience with Azure and Google Cloud Platform: Kubernetes, containers, IAM, networking, CI/CD, and API platforms.
- Familiarity with AIOps/MLOps tooling (MLflow, model registries, vector DBs, semantic layers, feature stores, monitoring).
- Strong knowledge of security, compliance, risk, and Responsible AI (RAI) for enterprise agent systems.
- Demonstrated ability to partner across engineering, data science, product, and security teams on complex AI platform architecture initiatives.
Key Responsibilities - Define and maintain the enterprise reference architecture for agentic platforms, including frameworks, tools, MCP components, registries, evaluations, orchestration, grounding, and observability.
- Establish architectural standards and best practices for agent design, tool integration, safety, telemetry, versioning, and lifecycle management.
- Provide architectural leadership to agentic platform engineering teams to ensure scalability, resiliency, and operational excellence.
- Design integrations with semantic layers, embeddings, vector search technologies, enterprise data products, and knowledge models to enable grounded agent behavior.
- Guide the architectural direction for low-code/no-code agent-building platforms to ensure governance, consistency, and ease of adoption.
- Partner with cloud, security, product, and enterprise architecture teams to ensure alignment with AI governance and Responsible AI principles.
- Define and support an agentic SDLC, including evaluation patterns, safety tests, regression gates, monitoring, and benchmarking.
- Assess new agentic frameworks, open-source standards, and orchestration tools to inform build-versus-buy decisions.
- Provide hands-on architectural guidance across engineering and data science teams to drive scalable, secure, cost-efficient agent deployments.
- Translate complex business requirements into technical design patterns and reusable platform capabilities that accelerate agent development.
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