AI Architect

Location:
Nashville, Tennessee (Client Office) or Remote with travel on need basis

We are seeking a highly experienced, client-facing AI Architect to lead the technical design and enterprise-wide deployment of scalable Agentic AI platforms. In this high-visibility role, you will act as the primary technical bridge between our engineering pods and the client’s internal technology teams. You will be responsible for defining the architectural blueprint, ensuring enterprise-grade security and reliability, and guiding complex multi-agent workflows into production on Google Cloud Platform (GCP).

• Technical Leadership & Blueprinting:
  Design and own the end-to-end architecture for production-grade Agentic AI systems, transitioning prototypes into secure, highly available enterprise applications.
• Stakeholder & Partner Alignment:
  Serve as the primary onsite technical liaison interfacing directly with the client’s Enterprise Architecture (EA) teams, AI Center of Excellence (AI CoE), Cloud Engineering, and Information Security boards.
• Cross-Partner
  
  Collaboration:
  
  Collaborate with external technology partners and marketplace vendors to establish best practices, integrate third-party APIs, and align on shared integration patterns.
• GCP Infrastructure Design:
  Architect robust AI infrastructure leveraging the GCP ecosystem, including Vertex AI, Gemini models, Cloud Run, Cloud Storage, and secure deployment pipelines.
• Agentic Workflow Design:
  Guide offshore engineering pods in the development of complex multi-agent orchestration (e.g., Lang Graph, Lang Chain), RAG pipelines, and automated evaluation frameworks.

• Production Agentic AI
  
  Experience:
  
  Proven, hands-on track record of successfully architecting, building, and deploying Agentic AI / Generative AI systems into live enterprise production environments (not just prototypes or hackathons).
• Deep GCP Cloud Expertise:
  Extensive experience with Google Cloud Platform, specifically Vertex AI, serverless deployments (Cloud Run/Functions), and IAM/Security configurations.
• Enterprise Architecture Acumen:
  Strong understanding of enterprise IT governance, network security, and compliance guardrails required to deploy AI in highly regulated environments.
• Communication & Executive Presence:
  Exceptional ability to translate complex AI engineering concepts into strategic business value for executive stakeholders, while negotiating technical trade-offs with client EAs and security officers.
• Core Engineering Background:
  Solid foundation in modern software engineering practices, CI/CD for machine learning (MLOps/LLMOps), API design, and observability (e.g., Lang Smith).