HPC AI Solution Architect (S2S)

PowerToFly; California (MO); On-site; USD 141,000 - 278,000; Full time

Job summary

Deloitte seeks a Lead Cloud Integrated Infra Engineer on the Silicon2Service team to design and deploy integrated GPU-accelerated AI factories and HPC infrastructure. You will translate client needs into complete architectures and delivery requirements, partnering with Deloitte AI specialists and ecosystem partners.

You will lead technical solution strategy for pursuits, own architecture narratives in proposals, and guide sizing, reference architectures, and go-to-market efforts while engaging

Qualifications

• 8+ years in infra architecture for large-scale platforms including design, implementation, operations, and optimization.
• 4+ years designing or delivering GPU-accelerated platforms for AI, ML, or HPC
• 3+ years Linux system administration in production environments
• 3+ years designing or operating distributed AI/HPC clusters in hybrid cloud including multi-GPU topologies
• 2+ years with high-performance networking or storage for AI/HPC
• 2+ years building containerized platforms using Kubernetes or OpenShift
• 2+ years automating infrastructure as code with Terraform and Ansible
• At least 2 end-to-end deployments of reference architectures in cloud or on-prem with security controls
• Experience in pre-sales or sales engineering, including discovery, solution demonstrations, and proposal/RFP contributions
• Ability to travel 50% on average

Responsibilities

• Lead architecture for pursuits including discovery and target-state design.
• Define reference architectures for on-prem, cloud, and hybrid GPU platforms across compute, network, storage, security, software and operations.
• Drive architecture trade-offs across performance, scalability, reliability, locality, total cost of ownership, time-to-value, and risk.
• Own the technical solution strategy in proposals and RFPs with architecture narrative and delivery approach.
• Facilitate client workshops and technical reviews translating engineering detail into executive-ready communications.
• Architect complex AI/HPC tech with focus on business outcomes, cost of quality, and long-term scalability.
• Engage with C-Suite leadership during sales and delivery, leading pre-sales discussions and shaping proposals.
• Support go-to-market strategies, including industry events and client briefings.

Skills

Infra architecture

GPU AI HPC

Linux admin

Distributed compute

Networking storage

Kubernetes/OpenShift

IaC Terraform/Ansible

Pre-sales

Travel 50%

Job description

Lead Cloud HPC- AI Infrastructure Architect(S2S)

As a Lead Cloud Integrated Infra Engineer on the Silicon2Service team in Deloitte's AI & Engineering practice, you will design and drive deployment of fully integrated architectures for GPU-accelerated AI factories and high-performance computing infrastructure in close partnership with Deloitte AI specialists and our ecosystem partners. You will shape end-to-end solutions-from discovery and reference architecture mapping through sizing and implementation. You will partner with Sales Executives, AI application specialists, delivery engineering, and managed services to help clients achieve measurable outcomes from private AI assets. You will lead technical solution strategy for pursuits and active opportunities and translate complex client needs into clear, complete solutions and delivery requirements.

Recruiting for this role ends on 10/3/2026.

Work you'll do

As a Lead Cloud Integrated Infra Engineer on the Silicon2Service team, you will be responsible for:

• Leading architecture for pursuits and active opportunities, including discovery, requirements, constraints, and target-state design
• Creatively defining reference architectures for on-premises, cloud, and hybrid GPU platforms across compute, network, storage, security, software and operations
• Driving architecture trade-offs and decisions across performance, scalability, reliability, locality, total cost of ownership, time-to-value, and risk
• Owning the technical solution strategy in proposals and RFPs, including architecture narrative, assumptions, dependencies, sizing guidance, and delivery approach
• Facilitating client workshops and technical reviews and translating engineering detail into executive-ready communications
• Architecting complex, innovative technology solutions with a focus on business outcomes, cost of quality, and long-term scalability and sustainability.
• Engaging with C-Suite client leadership during sales and delivery, including leading technical pre-sales discussions, shaping proposals, and supporting the closing of new business opportunities
• Supporting go-to-market strategies, including participation in industry events, conferences, and client briefings

The Team

The Silicon to Service team at Deloitte delivers end-to-end AI factories and advanced technology services that help organizations build, deploy, and operate large-scale, private AI and data platforms. We enable the next phase of enterprise AI adoption through private AI economics with cloud-like ese of use. Join this unique opportunity to work on innovative AI platforms and emerging technologies in the rapidly evolving AI market while solving complex enterprise problems for some of the world's largest organizations.

Qualifications

Required:

• 8+ years of experience in infrastructure architecture or engineering for large-scale platforms including design, implementation, operations, and optimization.
• 4+ years designing or delivering GPU-accelerated platforms for AI, ML, or high-performance computing
• 3+ years Linux system administration in production environments
• 3+ years designing or operating distributed compute clusters for AI/HPC in hybrid cloud setups, including multi-GPU topologies, partitioning, scheduler integration, and scalability for edge-to-cloud workloads.
• 2+ years with high-performance networking or storage for AI/HPC
• 2+ years building containerized platforms using Kubernetes or Red Hat OpenShift, including GPU operators/drivers, CUDA container runtime, and cluster lifecycle automation
• 2+ years automating infrastructure as code(IaC) with tools like Terraform and Ansible
• At least 2 end-to-end deployments of reference architectures in the cloud or on-prem, including variants with security controls, network segmentation, operational runbooks, and validation testing
• Experience in pre-sales or sales engineering, including discovery, solution demonstrations, and proposal/RFP contributions
• Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve.
• Limited immigration sponsorship may be available.

Preferred

Preferred:

• 2+ years implementing AI/HPC cluster scheduling (Slurm and Kubernetes), including multi-tenant queues, quotas, and GPU-aware policies
• 2+ years supporting generative AI infrastructure patterns, including multi-node distributed training
• Experience with AI agents and frameworks
• Experience with high-throughput storage for AI/HPC
• Experience executing NVIDIA co-sell motions with OEMS (Dell, HPC, Lenovo), CSPs ( AWS, Azure, Google Cloud), or independent software vendors ( Run:ai, OpenShift, Weights & Biases)

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $141,200 to $278,300.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.