Principal AI Framework Architect

Ashley Furniture Industries•Tampa, FL

About The Position

The Principal AI Framework Architect is a senior individual contributor and technical authority responsible for the architecture, engineering, and evolution of an enterprise AI framework. The framework provides reusable capabilities and engineering patterns that enable teams to build and operate AI applications and agents securely, reliably, and at scale. This is a deeply hands-on role spanning system architecture, technical prototyping, software development, design reviews, and production problem-solving. Rather than owning a single product or implementation, the architect establishes the technical foundation, patterns, and abstractions that support a broad ecosystem of AI solutions. The role owns the technical direction of a reusable enterprise AI framework: a shared foundation for AI agents and applications to access models, tools, data, orchestration, governance, security, and operational capabilities. The position sits on the AI Platform team and partners with AI/ML Engineering, Platform Engineering, Cloud Engineering, Security, Enterprise Architecture, Data, Product, and application engineering teams. People management is not a primary responsibility.

Requirements

Significant experience designing and building enterprise-scale software frameworks, distributed systems, developer platforms, or comparable shared technical capabilities.
Strong hands-on software engineering skills in Python, Java, Go, or similar languages, with the ability to move from architecture to implementation.
Demonstrated experience with enterprise AI, generative AI, agentic systems, LLM integration, tool calling, and AI application architecture.
Strong understanding of cloud-native systems, APIs, microservices, containers/Kubernetes, asynchronous processing, distributed state, security, identity, and observability.
Ability to evaluate emerging AI technologies and translate them into durable enterprise engineering patterns.

Nice To Haves

Experience with durable workflow technologies such as Temporal.
Experience with policy-as-code.
Experience with MCP and A2A.
Experience with AI gateways or proxies.
Experience with OpenAI Agents SDK, agentic frameworks such as LangGraph or LangChain, or LlamaIndex.
Experience with LiteLLM.
Experience with RAG or GraphRAG.
Experience with Kubernetes.
Experience with AWS, Azure, or GCP AI services.
Experience with Terraform.
Experience with event streaming.
Experience with OpenTelemetry.
Experience with enterprise IAM.

Responsibilities

Define and own the architecture, technical patterns, interfaces, and evolution of the enterprise AI framework across runtime, orchestration, governance, security, identity, integration, state, and observability.
Make pragmatic build-versus-buy and technology decisions and establish clear boundaries with enterprise infrastructure, cloud, security, data, and platform services.
Design the framework for scalability, resiliency, extensibility, and portability as models, agent frameworks, protocols, and enterprise requirements evolve.
Build reference implementations and critical framework components; prototype high-risk approaches and validate architecture through working software.
Write and review production-quality code, establish reusable libraries, APIs, and patterns, and work directly with engineers to solve difficult technical problems.
Diagnose complex distributed-system, integration, performance, reliability, and security issues.
Design reusable patterns for agent execution, durable workflows, sessions, state, retries, recovery, human-in-the-loop interactions, and runtime controls.
Embed policy-based authorization, workload contracts, identity propagation, least privilege, default-deny/fail-closed controls, and auditable decisions into the framework.
Create consistent, governed paths for model and tool access, including support for evolving protocols and agent technologies.
Improve framework adoption through clear APIs, SDKs, reference architectures, templates, paved paths, and documentation.
Enable application and AI teams to build solutions faster using common framework capabilities for governance, security, reliability, and observability.
Balance standardization with extensibility so teams can innovate without fragmenting the enterprise AI ecosystem.
Serve as a senior technical partner to the Director of AI Framework and senior engineering leadership on architecture and technical decisions.
Lead design reviews, mentor engineers, challenge assumptions, identify technical risks early, and communicate complex architecture to technical and business audiences.
Influence through technical expertise and delivery rather than organizational authority.