Title: AI Architect – Agentic Platforms
Location: Cincinnati, OH (Hybrid – 3 days onsite/week)
Hire Type: Contract
The hiring manager would like someone local to Cincinnati or someone who is willing to relocate themselves
SUMMARY
The AI Enablement team at Kroger/84.51° is seeking an AI Architect – Agentic Platforms to define the architectural foundations that power Kroger’s enterprise agent ecosystem.
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.
10+ years’ experience in cloud and distributed systems architecture focused on scalability, reliability, observability, and performance.
7+ years designing enterprise AI/ML systems; 1+ years hands-on with GenAI, agentic workflows, RAG, LLM-based integrations, or multi-agent systems.
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 Google Cloud Platform, 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.
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