AI Infrastructure Architect

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. Data Protection & Governance Define AI data protection strategies including Data Loss Prevention (DLP), information governance, and secure handling of regulated or confidential data.

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 compliance frameworks such as SOC 2 or comparable governance standards.
• Exceptional written and verbal communication skills with experience producing executive-level architecture documentation and presentations.

Preferred Qualifications

• Experience with the Microsoft AI ecosystem, including:
• Microsoft Copilot
• Microsoft Purview
• Microsoft 365
• Microsoft Entra ID
• Microsoft tenant architecture and governance
• Experience designing secure AI environments for regulated or publicly traded organizations.
• Consulting or professional services experience delivering architecture strategy engagements.
• Experience creating enterprise architecture standards, governance frameworks, and reusable playbooks.

Deliverables
The AI Infrastructure Architect will be responsible for producing:

• Secure AI Connectivity Assessment
• Enterprise AI Reference Architecture
• Target-State Connectivity Model
• Authentication-First AI Access Framework
• Private/On-Premises LLM Evaluation and Recommendation
• AI Infrastructure Cost Analysis
• AI Security and Governance Recommendations
• Reusable Architecture Frameworks
• Enterprise AI Playbook for Portfolio Company Adoption