Principal AI Framework Architect

Principal Individual Contributor | Platform Architecture and Production Engineering

The Principal AI Framework Architect is a senior individual contributor and technical authority responsible for the architecture, engineering, and evolution of the enterprise AI framework. The framework provides reusable capabilities and engineering patterns that enable teams across the enterprise to build and operate AI applications and agents securely, reliably, and at scale.

This is a deeply hands-on role. The architect will move between system architecture, technical prototyping, software development, design reviews, and production problem-solving. Rather than owning a single product or implementation, this role establishes the technical foundation, patterns, and abstractions that enable a broad ecosystem of AI solutions.

Role Purpose

Own the technical direction of a reusable enterprise AI framework: the shared foundation for AI agents and applications to access models, tools, data, orchestration, governance, security, and operational capabilities without rebuilding those capabilities independently.

Core 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 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/patterns, and work directly with engineers to solve the hardest technical problems.
• Diagnose complex distributed-system, integration, performance, reliability, and security issues.

AI Runtime, Orchestration & Governance

• 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.

Developer Experience & Adoption

• Make the framework easier to consume through clear APIs, SDKs, reference architectures, templates, paved paths, and strong documentation.
• Enable application and AI teams to build new solutions faster while relying on common framework capabilities for governance, security, reliability, and observability.
• Balance standardization with extensibility so teams can innovate without fragmenting the enterprise AI ecosystem.

Technical Leadership

• Serve as the deepest 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 clearly across technical and business audiences.
• Influence through expertise and delivery rather than organizational authority; people management is not a primary responsibility.

Qualifications

• Significant multiple years’ 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, GenAI, 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.

Preferred Experience

• Experience with several of the following: durable workflow technologies (Example Temporal)+; policy-as-code; MCP and A2A; AI gateways/proxies; OpenAI Agents SDK,Agentic frameworks(LangGraph, LangChain), LlamaIndex; LiteLLM; RAG/GraphRAG; Kubernetes; AWS/Azure/GCP AI services; Terraform; Event streaming; OpenTelemetry; enterprise IAM.

What Success Looks Like

• The AI framework becomes a trusted, reusable foundation for building enterprise AI applications. The framework becomes the default path for production AI applications across participating engineering teams.
• Core capabilities are exposed through consistent interfaces and paved paths, allowing teams to build faster while inheriting governance, security, reliability, and observability.
• The framework evolves without being tightly coupled to a single model provider, agent framework, protocol, or infrastructure implementation.
• The most important architectural decisions are proven through working software, documented patterns, and production adoption.
• Teams across the enterprise can innovate on AI applications without repeatedly solving the same foundational technical problems.

Role Profile

• Career track: Senior Individual Contributor / Technical Leadership
• People management: Not a primary responsibility
• Organization : AI Platform team
• Key partners : AI/ML Engineering, Platform Engineering, Cloud Engineering, Security, Enterprise Architecture, Data, Product, and application engineering teams