Director Adaptive Network Infrastructure & Security

V2X•UNAVAILABLE, UNAVAILABLE

21d

About The Position

The Director of Adaptive Network Infrastructure & Security is a senior technical leadership role responsible for designing, implementing, and operating a modern, self-adapting, AI-optimized network that serves as the transport backbone for the organization’s enterprise AI Fabric. Reporting to the VP of Intelligent Automation and IT Operations, this leader will drive the transition to a Zero Trust architecture, manage all aspects of core networking and telecommunications, deploy Secure Access Service Edge (SASE) capabilities, and ensure the network is purpose-built to carry AI workloads — including low-latency inference traffic, and agent-to-agent communication. This role is critical to establishing a greenfield IT operational environment, building a network foundation that is resilient, AI-aware, and compliant from day one. In the AI-First IT model, infrastructure is no longer static and human-managed — it is dynamic, AI-optimized, and self-adapting. This director will lead that transformation.

Requirements

Bachelor’s degree in Information Technology, Computer Science, Network Engineering, or related field.
Ability to obtain and maintain CMMC Level 2 certification.
A minimum of twelve (12) years of experience in network engineering and/or security operations.
A minimum of five (5) years of experience in a senior or lead technical leadership capacity.
Demonstrated hands-on experience implementing Zero Trust architectures in enterprise environments.
Direct deployment experience with SASE platforms.
Strong background in SD-WAN, LAN/WAN,, and enterprise switching.
Familiarity with machine identity management and ZTNA policies for non-human actors (AI agents, automation pipelines).
Experience with network automation tools including Ansible, Terraform, and Python.
United States citizenship required.
Ability to travel to project and customer locations as needed.
Deep expertise in Zero Trust frameworks and SASE architecture.
Understanding of AI workload network requirements: low-latency inference, agent-to-agent traffic.
Knowledge of machine identity management and ZTNA policy design for AI agents and automated systems.
Proficiency in network automation and infrastructure-as-code methodologies.
Strong vendor management and contract negotiation capabilities.
Excellent communication skills with the ability to present technical concepts to executive audiences.
Demonstrated ability to lead cross-functional teams in complex, matrixed environments.

Nice To Haves

Master’s degree preferred.
Active Secret security clearance desired.
Experience with FedRAMP-authorized cloud networking platforms.
Familiarity with GPU cluster networking, InfiniBand, or RDMA over Converged Ethernet (RoCE).
Experience with AI-aware QoS and network traffic engineering for LLM inference workloads.
Experience designing or operating network infrastructure for AI or high-performance compute workloads preferred.
Experience in the government contractor/services industry preferred.

Responsibilities

Architect and lead the enterprise-wide implementation of a Zero Trust Network Access (ZTNA) framework aligned to NIST 800-207 and CMMC compliance requirements.
Define and enforce network segmentation, micro-segmentation, and least-privilege access policies across all environments — for human users, devices, and AI agents alike.
Integrate identity-aware networking principles to ensure all users, devices, automated workloads, and AI agents are continuously verified before gaining network access.
Extend Zero Trust policies to cover machine identities: define ZTNA access controls for AI agents, automation pipelines, and non-human actors operating within the network environment.
Collaborate with the CISO and security teams to embed security-by-design into all network architectures and lifecycle decisions.
Manage secrets, credentials, and sensitive configuration data securely.
Lead the evaluation, selection, and deployment of SASE platforms to unify network and security functions at the edge.
Implement Cloud Access Security Broker (CASB), Secure Web Gateway (SWG), and Firewall-as-a-Service (FWaaS) capabilities within the SASE framework.
Drive the migration to a cloud-delivered, identity-centric access model.
Manage ongoing performance tuning, policy management, and vendor relationships for all SASE technologies.
Ensure SASE policies explicitly address AI agent traffic, model API calls, and automated workload egress — not only human user sessions.
Design and operate the network as an AI transport fabric: architect low-latency, high-throughput network paths optimized for AI model inference traffic, LLM API calls, and real-time agent communication.
Define AI-aware Quality of Service (QoS) policies that prioritize AI workload traffic appropriately alongside standard enterprise traffic.
Collaborate with the AI Engineering team to ensure the network layer meets the performance, reliability, and latency requirements of the AI Fabric and agent orchestration platforms.
Monitor and optimize network performance for AI workloads using telemetry data, proactively identifying and remediating bottlenecks in the AI traffic path.
Oversee the operations of enterprise the campus network infrastructure.
Manage telecommunications infrastructure including voice, unified communications, and carrier relationships.
Ensure network availability, capacity, and performance meet 99.99% SLA targets across all sites and remote environments.
Lead network lifecycle management including hardware refresh, end-of-life planning, and vendor contract negotiations.
Champion network automation using Ansible, Terraform, and Python-based scripting to eliminate manual configuration and reduce operational toil.
Implement workflows for network configuration management, change control, and compliance auditing.
Deploy network monitoring and observability platforms to enable proactive incident detection and AI-driven automated remediation.
Integrate network telemetry data into the AIOps platform to support intelligent operations, anomaly detection, and predictive capacity planning.
Build toward a self-adapting network model where AI systems can dynamically adjust routing, segmentation, and QoS policies in response to real-time demand signals — with appropriate human oversight thresholds.
Ensure all network architectures and operations are compliant with NIST 800-171, CMMC Level 2, and applicable federal regulations.
Develop and maintain network security policies, standards, and runbooks that explicitly address AI agent network access and machine identity governance.
Participate in audits, assessments, and continuous monitoring programs to demonstrate compliance posture.
Provide executive-level reporting on network health, AI workload performance, risk posture, and strategic initiatives.