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