Network Architect – AI

Dubai / Abu Dhabi / Remote . Full Time | Flexible – 10+ years of high performance networking experience and must have AI data center experience. DeFiTech is seeking a Network Architect – AI with an excellent, demonstrable background in both AI infrastructure networking and secure enterprise networks. You will design, implement, secure, and optimize the connectivity that supports GPU clusters, distributed AI workloads, enterprise applications, and hybrid cloud environments. This is a hands‑on architecture role for someone who can connect business and workload requirements to practical designs, lead implementation, and resolve difficult production issues. You should be comfortable moving from architecture reviews and capacity models to switch configurations, Linux diagnostics, security policies, and performance testing. Your work will help provide AI teams with reliable, high-performance infrastructure while protecting enterprise systems, sensitive data, and administrative access. You will work closely with AI and machine learning engineers, cloud and platform teams, cybersecurity specialists, infrastructure engineers, and technology stakeholders. Strong experience in BOTH AI networks and enterprise secure networks is essential. Candidates must be able to explain their own contribution to real deployments, the design choices they made, the problems they resolved, and the results they measured. What you will own You will own network architecture and technical design across AI infrastructure and its enterprise connectivity, from requirements and proof of concept through deployment, acceptance, and operational handover. You will recommend standards, evaluate options, document tradeoffs, and guide implementation in collaboration with the relevant service and security owners. The scope includes GPU compute fabrics; AI storage, application, and management connectivity; enterprise data center and office networks; secure cloud connectivity; and the network controls that connect these environments safely. The precise platform mix and deployment scale will be discussed during recruitment.

Key Responsibilities
AI network architecture and capacity planning
- – Translate training, fine-tuning, inference, data ingestion, and model-serving requirements into network designs with clear capacity, latency, resilience, security, and cost assumptions.
- – Design scalable leaf-spine, Clos, or other workload-appropriate topologies. Evaluate oversubscription, bisection bandwidth, port counts, uplink capacity, expansion paths, and failure domains.
- – Assess Ethernet with RDMA over Converged Ethernet version 2 (RoCEv2) and InfiniBand options against workload behavior, operational skills, ecosystem compatibility, and total cost of ownership.
- – Distinguish GPU scale-up interconnects such as NVLink from scale-out networks between servers. Coordinate GPU, NIC, PCIe, and NUMA topology requirements with compute specialists.
- – Plan appropriate separation or controlled convergence of GPU compute, storage, application access, in‑band management, and out‑of‑band management networks.
- – Specify switching, network interface cards, optics, cabling, link speeds, and redundancy. Evaluate 100/200/400/800 Gb/s connectivity where justified by the workload and supported platforms.
- – Coordinate rack connectivity, fiber and copper choices, transceiver compatibility, cable distances, physical diversity, and network equipment power requirements with data center teams.
- – Produce phased growth plans that accommodate additional GPU nodes, storage capacity, tenants, sites, and services without avoidable redesign. Ownership of:
- AI fabric deployment and performance optimization
- Enterprise network architecture and resilience
- Enterprise security and protection of AI infrastructure
- Hybrid cloud and container networking
- Automation, visibility, and operational readiness
- Architecture governance and technical leadership Expected Experience and

Qualifications:
- – A minimum of 5 years of relevant professional experience in network engineering, network architecture, data center networking, or closely related infrastructure roles, including substantive design and implementation responsibility.
- – Excellent hands‑on experience in both AI infrastructure networking and enterprise secure networks, supported by specific deployment examples.
- – Practical experience designing, deploying, or materially optimizing a multi‑node GPU or AI cluster network. Be prepared to discuss topology, transport, node or GPU scale, link speeds, workload characteristics, security controls, and validation methods.
- – Strong practical experience with RoCEv2/Ethernet or InfiniBand fabrics, together with the ability to explain the architectural differences, tradeoffs, and appropriate use cases of both.
- – Demonstrated understanding of RDMA, distributed GPU communication, congestion management, host networking, and the relationship between fabric behavior and AI wo