Head of AI Inference & MLOps

Deeter Analytics•Austin, TX

14d•Onsite

About The Position

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. This is not a traditional datacenter operations role. We are hiring the person who will make the racks make money. This leader will own the strategy and execution required to turn rack-scale GPU infrastructure into a profitable inference business: selecting the right models, runtimes, orchestration stack, routing layer, pricing strategy, customer segments, and marketplace relationships to maximize revenue, uptime, and utilization. The right candidate understands that raw compute is not the business. Monetized tokens, latency-adjusted utilization, and gross margin are the business. 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 design and run the inference platform that determines how our GB300 NVL72 racks are monetized in the real-time market. That may include direct enterprise workloads, marketplace distribution, API-based reselling, model hosting, fine-tuned/private deployments, and emerging inference channels. You should know what makes money on modern inference hardware, what does not, and why. You should be able to answer questions like: Which open-weight and commercial-compatible models should run on this hardware first? How should workloads be split between premium low-latency serving, bulk throughput, reserved capacity, and experimental capacity? Should we route through third-party marketplaces, sell directly, or do both? What software stack gives us the best performance per watt, per GPU, and per dollar of capex? How do we maximize realized revenue rather than theoretical benchmark performance? How do we scale from a 7MW launch to a repeatable 50MW AI factory operating model?

Requirements

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 SGLang 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
Ability 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

Nice To Haves

Experience monetizing large-scale NVIDIA GPU infrastructure
Experience with rack-scale or cluster-scale inference environments
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
Experience 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

Responsibilities

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 inference 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. OpenRouter provides a unified API and provider aggregation layer, while Inference.net offers an OpenAI-compatible API experience around model access and deployment, making both relevant examples of the ecosystem this role would evaluate. (OpenRouter)
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
Build 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