Senior Systems Engineer

Senior System Engineer

We are looking for a customer-facing systems engineer to help neo-cloud providers design and deploy production AI Factories at scale. This role covers the full AI Factory infrastructure solution: compute, high-speed networking, storage, orchestration, rack design, power, cooling, deployment, and operational readiness. Customers may purchase AI Factories with thousands of accelerators, so the role requires practical understanding of system-level constraints, not just individual server specifications.

You will work with Dell AI solution sales team members, product teams, partners, and customer engineering and data-center teams to neocloud requirements into turn-key Dell AI Factory designs that adhere to the NVIDIA, AMD, or Intel reference architectures.

You will

• Work with neocloud operators to gather technical requirements that can be shaped into an end-to-end Dell AI Factory design, ensuring that it meets the standards set by silicon partners NVIDIA or AMD at scale

• Translate workload requirements into an infrastructure design covering GPU type and topology, CPU and memory, GPU interconnect, network fabric, storage, data pipelines, orchestration, management, and services.

• Size and shape rack-scale - AI Factory deployments, including assumptions for utilization, growth, failure domains, availability, tenant isolation, and operational support. Maintain trusted relationships with technical and executive stakeholders without losing technical accuracy.

• Assess data-center readiness and identify blockers before a solution is proposed. This includes utility capacity, electrical distribution, rack power density, redundancy, floor and rack constraints, airflow, liquid-cooling requirements, direct-liquid-cooling infrastructure, coolant distribution units, and commissioning requirements.

• Hold credible technical discussions with customer facilities, data-center, platform, and infrastructure teams about power and thermal limits. You do not need to be a facilities engineer, but you must be able to identify risks, quantify constraints, and involve the right specialists. execute pursuit plans.

• Support customer evaluations of performance, power efficiency, cooling approach, capacity expansion, resiliency, manageability, and total cost of operation.

• Build reference architectures, sizing inputs, bills of materials, technical proposals, deployment assumptions, risk registers, and solution handoff documents. Lead architecture workshops, technical briefings, proof-of-concept and proof-of-value engagements, performance discussions, and acceptance-criteria definition.

Essential skills:

• 5+ years of experience in systems engineering, solution architecture, technical presales, technical sales, HPC, AI infrastructure, data-center infrastructure, or a related field. Experience designing, sizing, deploying, or supporting HPC infrastructure or multi-rack AI Factory.

• Strong understanding of AI infrastructure components and their interactions: GPU compute, CPU and memory, high-speed networking, storage, data movement, orchestration, monitoring, and platform operations.

• Practical knowledge of data-center power and thermal considerations, including rack power, electrical distribution, redundancy, power constraints, airflow, and liquid cooling. Ability to reason about cluster-level performance, bottlenecks, failure domains, expansion plans, and operational tradeoffs.

• Ability to lead technical discovery and communicate clearly with engineers, architects, facilities teams, procurement stakeholders, and executives and to produce clear technical recommendations, architecture diagrams, sizing assumptions, and customer-facing proposals.

• Experience working across sales, product, engineering, delivery, and partner organizations in a matrixed environment.

Desired skills:

• Hands on experience with Dell PowerEdge or HPC Validated Designs, either through deployment or operation. Experience with neo-cloud providers, GPU cloud platforms, AI factories, HPC environments, colocation providers, or large data-center operators.

• Experience with deployments involving hundreds or thousands of GPUs and with high-performance Ethernet, RDMA, InfiniBand, HPC fabric design, or other scale-out networking technologies.

• Experience with direct liquid cooling, rear-door heat exchangers, coolant distribution units, or other high-density cooling approaches and experience with Kubernetes, Slurm, containers, cluster management, telemetry, or AI platform operations and sovereign AI, data-residency, security, or regulated-environment requirements.