Network Data Center Engineer

Texas Instruments•Dallas, TX

About The Position

The Network Data Center Engineer designs, builds, and evolves scalable, resilient, and highly automated data center network architectures that enable private cloud and hybrid cloud platforms. This is a senior individual-contributor role responsible for defining technical direction and architecture standards, as well as delivering hands-on engineering across modern, multi-vendor data center environments. The role emphasizes large-scale data center networking, AI- and GPU-based workloads, cloud-like infrastructure, automation, and Infrastructure as Code, with particular expertise in Arista-based environments. The engineer serves as a technical authority and architectural advisor for data center networking initiatives. They partner with cloud, SRE, security, application, compute, and platform teams; review designs; influence standards; mentor engineers without direct people management; and participate in complex incident analysis and long-term architectural remediation.

Requirements

Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
At least 8 years of experience in data center networking or infrastructure engineering.

Nice To Haves

Deep expertise in EVPN/VXLAN-based architectures and large-scale routed fabrics.
Strong hands-on experience with Arista EOS, CloudVision (CVP, CVaaS), and AVD.
Experience designing networks for private cloud, AI, GPU, or high-performance computing environments.
Proven experience with automation and Infrastructure as Code using Python, Terraform, Ansible, AVD, or equivalent.
Strong understanding of BGP, routing at scale, network segmentation, and resiliency patterns.
Experience designing or operating GPU fabrics or AI platforms at scale.
Familiarity with lossless networking, RDMA, or high-bandwidth fabrics.
Experience supporting Kubernetes, OpenShift, or cloud-native platforms.
Hands-on experience with network telemetry and AI-driven observability tools.
Experience integrating Arista platforms within multi-vendor data center environments.
Strong documentation and architecture-review skills.

Responsibilities

Design, architect, and standardize data center networks optimized for scale, resiliency, and performance.
Design Layer 2 and Layer 3 architectures using EVPN/VXLAN for multi-tenant, cloud-ready environments.
Lead architecture decisions in a multi-vendor network ecosystem, with a strong emphasis on Arista Networks platforms.
Develop reference architectures, design patterns, and technical standards aligned with Arista Validated Designs (AVD) and enterprise best practices.
Serve as a technical authority for Arista EOS-based data center fabrics and integrate other vendors as required.
Design and operate network fabrics using Arista CloudVision (CVP) for centralized management, telemetry, and lifecycle operations.
Use AVD to enable consistent, repeatable, automated fabric deployments, and evaluate Arista solutions as part of a broader multi-vendor strategy.
Design network fabrics for AI/ML training and inference, large-scale GPU clusters, and distributed compute platforms, optimizing east-west traffic, bandwidth, latency, throughput, and deterministic performance.
Collaborate with compute and platform teams to align network designs with AI and GPU workload requirements.
Enable private cloud and cloud-like self-service platforms; integrate networking with virtualization, container, and platform environments.
Support secure, scalable hybrid connectivity between private data centers and public cloud environments, with designs aligned to segmentation, Zero Trust networking, and security best practices.
Drive automation-first networking and implement Infrastructure as Code solutions using Python, Terraform, Ansible, AVD, or similar frameworks.
Build reusable automation modules, pipelines, and workflows for network provisioning and lifecycle management; promote declarative, version-controlled network operations.
Define and implement network observability strategies using streaming telemetry, flow data, logs, and metrics.
Use CloudVision telemetry and AI/ML-driven analytics to detect anomalies, predict failures, and optimize performance; integrate observability with SRE and platform monitoring ecosystems.
Use data-driven insights to improve reliability, capacity planning, and incident response.
Review designs, influence standards, mentor engineers, partner with cloud, SRE, security, and application teams, and participate in complex incident analysis and architectural remediation.