Hire AI Infrastructure Engineers | TeamStation AI

Modern AI workloads require a specialized infrastructure that can handle massive data processing and GPU-intensive computation. Our AI Infrastructure Engineers are experts in building and managing this platform. We provide talent vetted for their expertise in Kubernetes, GPU management (NVIDIA), distributed training, and scalable model serving.

Are your GPU resources underutilized and expensive?

The Problem

GPUs are expensive, and without a proper scheduling and orchestration system, they are often left idle, leading to massive waste in cloud spending. Managing GPU drivers and dependencies across a cluster is also a major operational headache.

The TeamStation AI Solution

Our engineers are experts in GPU orchestration on Kubernetes. They are vetted on their ability to use tools like the NVIDIA device plugin and GPU-aware schedulers to maximize utilization and efficiently share GPU resources across multiple teams and workloads, dramatically reducing costs.

Proof: Improved GPU utilization and reduced cloud spend.
Is deploying a new model a complex, manual process?

The Problem

Getting a trained model into a production environment that can handle real-time inference traffic at scale is a huge challenge. It requires knowledge of containerization, networking, and high-performance serving frameworks that most data scientists don't possess.

The TeamStation AI Solution

Our AI Infrastructure Engineers are experts in model serving. They can containerize any model and deploy it on a scalable, high-performance inference server like NVIDIA Triton or KServe, complete with auto-scaling, health checks, and traffic management.

Proof: Automated, scalable, and high-performance model serving.

How We Measure Seniority: From L1 to L4 Certified Expert

We don't just match keywords; we measure cognitive ability. Our Axiom Cortex™ engine evaluates every candidate against a 44-point psychometric and technical framework to precisely map their seniority and predict their success on your team. This data-driven approach allows for transparent, value-based pricing.

L1 Proficient

Guided Contributor

Contributes on component-level tasks within the AI Infrastructure Engineer domain. Foundational knowledge and learning agility are validated.

Evaluation Focus

Axiom Cortex™ validates core competencies via correctness, method clarity, and fluency scoring. We ensure they can reliably execute assigned tasks.

$20 /hour

$3,460/mo · $41,520/yr

± $5 USD

L2 Mid-Level

Independent Feature Owner

Independently ships features and services in the AI Infrastructure Engineer space, handling ambiguity with minimal supervision.

Evaluation Focus

We assess their mental model accuracy and problem-solving via composite scores and role-level normalization. They can own features end-to-end.

$30 / hour

$5,190/mo · $62,280/yr

± $5 USD

L3 Senior

Leads Complex Projects

Leads cross-component projects, raises standards, and provides mentorship within the AI Infrastructure Engineer discipline.

Evaluation Focus

Axiom Cortex™ measures their system design skills and architectural instinct specific to the AI Infrastructure Engineer domain via trait synthesis and semantic alignment scoring. They are force-multipliers.

$40 / hour

$6,920/mo · $83,040/yr

± $5 USD

L4 Expert

Org-Level Architect

Sets architecture and technical strategy for AI Infrastructure Engineer across teams, solving your most complex business problems.

Evaluation Focus

We validate their ability to make critical trade-offs related to the AI Infrastructure Engineer domain via utility-optimized decision gates and multi-objective analysis. They drive innovation at an organizational level.

$50 / hour

$8,650/mo · $103,800/yr

± $10 USD

Pricing estimates are calculated using the U.S. standard of 173 workable hours per month, which represents the realistic full-time workload after adjusting for federal holidays, paid time off (PTO), and sick leave.

Core Competencies We Validate for AI Infrastructure Engineer

Kubernetes for AI/ML workloads
GPU Management (NVIDIA drivers, device plugins)
Distributed Training Frameworks (e.g., Horovod)
High-Performance Model Serving (e.g., Triton Inference Server)
Storage for Large Datasets (e.g., S3, distributed file systems)

Our Technical Analysis for AI Infrastructure Engineer

Candidates must design the infrastructure for a complete ML platform on Kubernetes. This includes setting up a cluster with GPU nodes, configuring it for distributed training, and deploying a high-performance inference server like NVIDIA Triton. We assess their ability to solve common infrastructure challenges related to networking, storage, and resource management in an AI context.

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