Head of AI Inference & MLOps

About the role

We are building a high-density AI datacenter campus outside Austin, Texas, beginning with approximately 7MW of NVIDIA GB300 NVL72 infrastructure and scaling to 50MW+. The initial deployment is designed around real-time inference, reasoning, and high-value AI serving workloads, with a focus on monetizing capacity in live markets rather than simply leasing powered space.

Reports to

Founders / Executive Team

The Role

We need a senior operator-builder who can sit at the intersection of: AI infrastructure, inference performance engineering, model serving and routing, marketplace monetization, customer / partner integration, revenue optimization.

You will own

• Build and lead the inference monetization strategy for our first 7MW deployment and expansion to 50MW
• Define the technical and commercial operating model for turning GB300 NVL72 racks into revenue-producing assets
• Evaluate and implement the model serving stack, scheduling layer, inference engine, observability stack, and API platform
• Select and optimize the mix of workloads across: real-time inferencing, reasoning workloads, premium low-latency API traffic, batch / overflow workloads, dedicated enterprise deployments, private/fine-tuned model hosting
• Identify the best go-to-market channels for capacity monetization, including direct sales and marketplace/API distribution partners
• Develop strategy for integration with platforms such as OpenRouter-style aggregation, OpenAI-compatible endpoints, and other inference distribution channels where appropriate
• Own benchmarking methodology based on actual profit and production metrics, not vanity metrics
• Drive workload placement decisions based on revenue per rack, revenue per GPU-hour, revenue per MW, latency targets, and customer value
• Partner with datacenter engineering, networking, and facilities teams to ensure the physical plant supports the intended software monetization strategy
• Create pricing, SLAs, utilization strategy, and customer segmentation framework
• Create dashboards and control systems for: utilization, queue health, latency, token throughput, margin by workload, failure rate, realized revenue by cluster / rack / model / customer
• Lead decisions around multi-tenant vs single-tenant deployments, reserved vs on-demand capacity, and when to prioritize direct contracts over marketplace traffic
• Build and manage the team required to scale this function over time

Required Experience

• Significant experience in production AI/LLM inference, MLOps, model serving, or AI infrastructure monetization
• Proven experience running or scaling GPU-backed inference systems in production
• Strong understanding of modern inference runtimes, serving frameworks, and optimization techniques
• Experience with one or more of: vLLM, TensorRT-LLM, GLaM, Ray Serve, Triton Inference Server, Kubernetes-based GPU orchestration, custom routing / scheduler layers
• Experience optimizing for real-world production metrics such as throughput, latency, GPU utilization, availability, and cost efficiency
• Strong understanding of LLM inference economics, including tradeoffs among model size, quantization, latency, throughput, memory footprint, and customer willingness to pay
• Experience building or managing API-based AI platforms or inference products
• Able to translate infrastructure capability into a pricing and product strategy
• Experience working with enterprise customers, developer platforms, or AI marketplaces
• Strong technical judgment on model selection, infrastructure topology, and commercialization strategy

Preferred Experience

• Experience monetizing large-scale NVIDIA GPU infrastructure
• Background in both technical operations and business strategy
• Familiarity with AI inference aggregators, routing platforms, and model marketplaces
• Experience designing multi-tenant GPU systems with strong isolation and predictable performance
• Familiarity with advanced observability, token-level metering, cost accounting, and SLA enforcement
• Familiarity with reasoning-model workloads, agentic inference, multimodal inference, and future high-density AI factory architectures
• Experience supporting OpenAI-compatible APIs and enterprise private deployments

What Success Looks Like

• In the first 3–6 months, stand up a production inference platform for our initial GB300 NVL72 deployment, recommend the highest-value initial workloads and monetization channels, launch a repeatable commercialization strategy for rack capacity, establish a clear performance and revenue measurement framework, identify where we should sell capacity: direct, through marketplaces, via strategic partners, or through a hybrid approach, turn the first cluster into a measurable cash-generating operation
• In the first 12 months, build the operating playbook for scaling from 7MW to 50MW, increase utilization without destroying margins or SLA quality, improve realized revenue per rack through model, routing, pricing, and customer mix optimization, establish the company as a serious real-time inference operator, not just a GPU owner

Compensation

Competitive salary, bonus, and equity participation tied to the scale, importance, and revenue generated from the role.