Senior Solutions Engineer, AI Infrastructure

Role Description

We're looking for a deeply technical Solutions Architect to help customers design, evaluate, and deploy infrastructure for large-scale AI, HPC, analytics, and data-intensive workloads. This is a customer-facing technical role for someone who has lived inside production infrastructure. You may have been a platform engineer, infrastructure engineer, SRE, MLOps engineer, AI infrastructure engineer, storage engineer, cloud engineer, or HPC systems engineer. What matters most is that you have built, operated, or architected real systems, and can bring that credibility into customer conversations.

Our customers are building infrastructure at serious scale:

• GPU clusters
• High-performance storage systems
• Kubernetes platforms
• Distributed training environments
• Inference platforms
• Data pipelines
• Lakehouses
• Large enterprise systems

You'll help them reason about architectures involving:

• 10,000+ GPUs
• 100PB+ of storage
• High-performance networking
• Distributed filesystems
• Orchestration layers
• Demanding production workloads

You'll own technical discovery, architecture design, PoC planning, competitive positioning, and customer technical strategy. You'll work from the first whiteboard session through evaluation, deployment planning, and production success. You'll also partner closely with product and engineering teams to bring field feedback into the roadmap.

We're looking for someone who can go deep technically, communicate clearly, operate without a rigid playbook, and translate complex infrastructure into customer outcomes.

Qualifications

• 8 to 12+ years of technical experience, with significant hands-on infrastructure experience.
• Experience building, operating, or architecting production platform infrastructure.
• Strong understanding of Linux kernel implementation details, distributed systems including PAXOS and raft, storage implementations details like NAND or write amplification, networking store/forward, load balancing designs, and production operations.
• Experience with one or more of: GPU infrastructure, large scale HPC systems, Kubernetes platforms from scratch, MLOps, storage systems, cloud infrastructure, data platforms, or large-scale enterprise infrastructure.
• Ability to communicate credibly with engineers, architects, technical executives, and business stakeholders.
• Strong discovery, problem-solving, and systems debugging skills.
• Comfort operating in ambiguous, fast-moving environments.
• Interest in customer-facing technical work, solution design, and business outcomes.

Requirements

• Experience with large-scale GPU clusters, distributed training, inference infrastructure, or AI platforms.
• Experience with petabyte-scale storage or high-performance data systems.
• Experience with Kubernetes, Slurm, Ray, Spark, or other orchestration / scheduling systems.
• Domain Expertise with one or more of these - Lustre, Ceph, Weka, BeeGFS, GPFS, VAST, object storage, or distributed filesystems.
• Experience with InfiniBand, RoCE, RDMA, high-performance Ethernet, or NVIDIA/Mellanox networking.
• Direct Experience with CUDA, NCCL, DCGM, GPUDirect, checkpointing, dataset staging, or model-serving infrastructure.
• Experience across multiple industries or customer environments.