Hire AI Infrastructure Engineers
Who Scale GPUs, Inference, and Data Pipelines

You can usually start the hiring conversation immediately and receive a shortlist within 24 hours after we understand your product, stack, timeline, and seniority needs. The goal is not to send resumes quickly; it is to send AI Infrastructure Engineers who match the outcome, risk profile, and communication bar for the role.

Yes. You interview the shortlisted engineers before committing. We recommend using the interview to test GPU utilization, inference serving, scaling strategy, model storage, networking, cost control, and reliability tradeoffs. That makes the selection practical for a CTO instead of resume-led.

The first week should produce visible proof that the engineer understands your workload and can move an operational metric. You should see an AI infrastructure improvement with capacity notes, benchmark data, cost or latency impact, reliability risks, and next scaling steps. If progress is unclear, you should know that early, not after a long contract cycle.

A strong hire should produce AI infrastructure that supports serving, GPUs, queues, storage, networking, scaling, observability, and cost controls. The outcome should be measurable through throughput, tokens per second, p95 latency, GPU utilization, queue depth, error rate, cold-start time, rollout success, cost per request, and incident recovery time.

Quality is managed through senior screening, role-specific interview criteria, infrastructure or architecture review, documented decisions, and delivery checkpoints. For AI infrastructure work, we look for proof across GPU cluster design, inference serving, Kubernetes, Terraform, Helm, GitOps, load testing, monitoring, capacity planning, cost controls, rollout safety, and runbooks.

Yes. The engineer joins your tools, repositories, standups, issue trackers, review process, and communication channels. For AI Infrastructure Engineer work, we define the operating model explicitly: capacity plans, access rules, deployment manifests, benchmark notes, cost visibility, monitoring rules, rollout plans, and operational runbooks are part of delivery.

Yes. Devlyn works with distributed teams and plans overlap windows for interviews, standups, reviews, and escalation. For AI Infrastructure Engineer engagements, the communication rhythm is tied to the proof points that matter: throughput, latency, GPU utilization, cost per workload, queue depth, error rate, and release reliability.

NDA and IP assignment are handled before onboarding. Access is scoped to the tools, repositories, datasets, systems, or environments required for the AI Infrastructure Engineer scope, and sensitive work is governed through your security rules, audit expectations, and approval process.

Use the risk-free trial to evaluate whether the engineer can profile a workload, reason about GPU utilization, improve inference serving, define scaling strategy, handle model storage and networking, expose cost tradeoffs, and communicate reliability risks clearly. If the fit is wrong, we replace the engineer within 48 hours instead of forcing you through a long notice period or another sourcing cycle.

You can start with one specialist, add adjacent roles, or move into a pod model depending on the scope. Common expansion paths include product engineering, platform, data, security, QA, DevOps, or architecture support around the core AI Infrastructure Engineer work.

Typical options include GPU & Inference Optimization ($18,000 fixed scope) 3 weeks, senior infra engineer, Senior AI Infrastructure Engineer ($7,500/mo) Full-time, 5–10+ years, AI Infra + MLOps + SRE ($26,000/mo) 3-person pod, 3–6 months. We confirm the right model after discovery so you can compare dedicated hiring, a focused sprint, or a small pod against the risk and timeline of your actual AI Infrastructure Engineer requirement.

We can support both models. If you already have strong product and platform leadership, the engineer can plug into your process. If you need more structure, Devlyn can add delivery oversight, sprint planning, reporting, and senior technical review around serving, GPUs, queues, storage, networking, scaling, observability, and cost controls.

Devlyn reduces the hidden work of sourcing, vetting, onboarding, replacing, and governing specialist engineering talent. For AI infrastructure, that matters because the real risk is a product constrained by expensive GPUs, slow inference, poor autoscaling, weak isolation, fragile rollouts, or unreliable model serving. You get a shorter path to qualified candidates and a trial structure focused on measurable operational proof.

Devlyn is a better fit when AI infrastructure affects production systems, customer latency, reliability, security, cloud cost, GPU utilization, or long-term maintainability. You get vetting, replacement support, delivery governance, IP protection, and continuity around the parts freelancers often skip: benchmark discipline, rollout safety, monitoring, runbooks, cost dashboards, and capacity planning.

An AI Infrastructure Engineer is usually the right hire when AI workloads are stressing cost, latency, capacity, reliability, or deployment safety. Common use cases include LLM inference platforms, GPU cluster design, vLLM or Triton serving, Kubernetes GPU scheduling, model rollout and canary strategy, training and fine-tuning infrastructure, embedding pipelines, batch inference queues, hybrid AI deployment, cost optimization, observability, and SLO design. If discovery shows you mainly need MLOps pipelines, app integration, or general cloud DevOps, we will say that before you hire.