AI Infrastructure Architect

Position Summary

We are seeking an experienced AI Infrastructure Architect to lead the design, evaluation, and documentation of secure enterprise AI infrastructure for a publicly traded organization. This role serves as the technical lead for Secure AI Connectivity , defining the architecture, security boundaries, and governance required to safely adopt enterprise AI technologies while protecting sensitive corporate information.

This is a highly consultative, advisory role focused on architecture, strategy, and executive guidance—not hands-on implementation. The successful candidate will work closely with senior technology leadership to validate existing designs, recommend future-state architectures, evaluate private AI deployment options, and develop reusable frameworks that can be replicated across multiple portfolio companies.

**Key Responsibilities

Secure AI Architecture Strategy**

• Lead the Secure AI Connectivity workstream by defining enterprise AI connectivity strategies that balance innovation, security, governance, and cost.
• Assess existing AI infrastructure and validate current “AI walled garden “ or isolated AI network designs against enterprise security, compliance, and data governance requirements.
• Identify architectural risks and recommend enhancements or alternative approaches where appropriate.

Enterprise AI Connectivity Design

• Design and document secure reference architectures for enterprise AI adoption.
• Define authentication-first connectivity models that enable secure AI access without broad enterprise content crawling.
• Establish architecture patterns that clearly separate identity-based authentication from enterprise content authorization.
• Document network segmentation, isolation strategies, secure API gateway patterns, egress controls, and connectivity boundaries.

Private AI & LLM Architecture

• Evaluate commercial API-based LLM platforms alongside private and self-hosted LLM deployment models.
• Assess infrastructure requirements including GPU capacity, inference platforms, storage, networking, and operational considerations.
• Perform cost-benefit analysis comparing token consumption costs with on-premises infrastructure investments.
• Develop recommendations balancing security, performance, scalability, and total cost of ownership.

Data Protection & Governance

• Define AI data protection strategies including Data Loss Prevention (DLP), information governance, and secure handling of regulated or confidential data.
• Evaluate how enterprise AI platforms store, process, retain, and expose organizational information.
• Ensure architectural recommendations align with enterprise security controls and governance frameworks.

Standards, Frameworks & Playbooks

• Produce comprehensive architecture documentation including:
• Secure AI Connectivity Assessment
• Target-State Reference Architecture
• Connectivity Standards
• Architecture Decision Records
• Technology Evaluation Matrix
• Private LLM Recommendation Summary
• Develop reusable frameworks, implementation guidance, and playbooks that can be leveraged across multiple enterprise portfolio companies.

Executive Advisory

• Present architecture recommendations, trade-offs, and technology options to executive leadership and senior IT stakeholders.
• Facilitate architecture discussions with security, infrastructure, and business leaders.
• Communicate complex technical concepts clearly to both technical and executive audiences.

Required Qualifications

• 10+ years of experience in enterprise infrastructure, cloud, network, or security architecture.
• Demonstrated experience designing secure enterprise architectures for large organizations.
• Deep expertise in:
• Enterprise network segmentation and isolation
• Secure API gateway architecture
• Egress controls
• Identity and Access Management (IAM)
• Enterprise authentication and authorization
• Strong experience with Microsoft Entra ID (Azure Active Directory), Single Sign-On (SSO), and enterprise identity architecture.
• Experience evaluating or deploying enterprise AI platforms, including:
• Commercial API-based LLM services
• Private or self-hosted LLM environments
• AI inference infrastructure
• GPU sizing and infrastructure planning
• Token consumption and AI cost optimization
• Knowledge of enterprise Data Loss Prevention (DLP), information protection, and AI governance.
• Familiarity with enterprise security and compli…