Hire AI Infrastructure Engineers Who Keep Training and Inference Running at Scale
Hire AI infrastructure engineers from Entrans and get senior talent for GPU clusters, Kubernetes, and model serving. They build the pipelines, deployment automation, and monitoring your AI workloads depend on. You interview in days and onboard in 48 to 72 hours.
Why Market Leaders Choose Entrans AI Infrastructure Engineers
The model is rarely the bottleneck. Cost per inference, cluster utilization, and a deploy that takes three weeks are. Our AI infrastructure engineers are hired to fix that layer.
1. Platform Experience, Not Slideware
Every engineer we place has run AI workloads in production. They have sized GPU nodes, chased a memory leak in a serving container, and cut a deploy cycle from weeks to hours.
2. Senior Talent, Screened on Systems
We test how candidates reason about throughput, latency, and failure. Each one walks through a real architecture decision and defends the tradeoff they made.
3. One Team From Cluster to CI/CD
Compute, storage, orchestration, deployment, and monitoring sit with the same team, backed by our enterprise cloud practice. There is no handoff gap between the infrastructure vendor and the ML team.
4. Security and Compliance Built In
Our engineers have worked inside healthcare and identity platforms where access control and audit trails are not optional. NDAs, scoped cloud access, and IAM policy are set up before day one.
5. Spend Control That Survives a Board Review
Right sizing, spot and reserved mixes, autoscaling, and quantized serving all get examined. You see where the GPU budget goes and what it would take to lower it.
Hire AI Infrastructure Engineers From Entrans Who Are Certified and Experienced
Entrans has delivered 150+ AI projects for 200+ enterprises with 500+ domain trained professionals behind them. Here is what an AI infrastructure engineer for hire handles once they join your team.
GPU and Compute Provisioning
Size, provision, and schedule GPU and CPU capacity across AWS, Azure, and GCP. Spot, reserved, and on demand mixes are chosen against real utilization data, not a guess.
Kubernetes and Container Orchestration
Run training and serving workloads on EKS, GKE, or AKS with Helm, node pools, and autoscaling tuned for bursty jobs. GPU scheduling and job queueing are part of the setup.
Training and Data Pipelines
Build orchestration with Airflow, Argo, or Kubeflow so training runs, backfills, and reprocessing happen on a schedule instead of by hand. Our DataOps and MLOps services team supports the same tooling.
Model Serving and Inference
Deploy with Triton, KServe, or vLLM, then tune batching, caching, and quantization until latency and cost per request hit your target.
Infrastructure as Code and CI/CD
Terraform, CloudFormation, and pipeline automation keep environments reproducible and make a rollback take minutes. Our DevOps and quality engineering team works the same way.
Observability, Reliability, and Cost
Prometheus, Grafana, and cloud native monitoring wired to real SLOs. You get alerts on latency, utilization, and spend, plus disaster recovery that has actually been tested.
Schedule Interviews With AI Infrastructure Engineers and Onboard Them Within 48 to 72 Hours
AI Infrastructure Development Technology Expertise
Compute and Accelerators
Orchestration and Containers
Serving, IaC, and Delivery
Data, Observability, and Cloud
Our Customer Success Stories
CI/CD and Kubernetes Platform for a Global IAM Provider
Industry: Cybersecurity and Identity Management
Technical Stack: Amazon EKS, AWS CloudFormation, CodePipeline, CodeBuild, CloudWatch, X-Ray, IAM, KMS
This identity platform shipped fast but could not do it safely. Every release had to clear strict security review, and the infrastructure had to scale without losing availability. Entrans built a secure CI/CD and Kubernetes platform on Amazon EKS, with CloudFormation for infrastructure as code and automated security scanning inside the release pipeline. Deployment related incidents fell 75%, and deployment cycles went from weeks to hours. Recovery drills now validate RTO and RPO on a schedule.
Event Driven AWS Platform for a Water Engineering Firm
Industry: Water and Environmental Engineering
Technical Stack: AWS Airflow, event driven pipelines, centralized data lake, Zoho IoT APIs, forecast model training
Desalination monitoring ran on isolated EC2 instances with file based, manual data processing. The team could not replay history or scale ingestion, and that capped forecasting. Entrans replaced it with an event driven pipeline on AWS Airflow feeding a central data lake, plus a forecast training engine and a goal seek simulation layer. IoT ingestion is now 100% automated, historical reprocessing runs at 99% reliability, and forecasting and optimization got 40% faster.
Designed for Enterprise Speed and Control
Hiring an AI infrastructure engineer through a job board takes months. Our process moves in days, and the final call stays with you.
1. Share Your Requirements
Tell us the workloads, your cloud, and the constraint that matters most: latency, uptime, or cost.
2. Get Curated Profiles (Within 24 to 48 Hours)
You get three to five matched profiles with platforms shipped, cloud depth, and availability.
3. Evaluate and Interview
Run your own technical screen. We schedule the calls around your calendar.
4. Onboard and Kickoff (Within 48 to 72 Hours)
Contracts, NDAs, and cloud access run in parallel. The engineer ships in week one.
5. Continuous Support and Scaling
Add a data engineering or site reliability specialist as the platform grows. A delivery manager stays on the account.
Our Hiring Models
Dedicated AI Infrastructure Engineers
Hire a dedicated AI infrastructure engineer who works only on your platform, from cluster setup through serving and cost tuning. Contract to hire is available when you want them in house later.
Team Augmentation
Add AI infrastructure engineers to the platform or ML team you already run. They use your repos, your on call rotation, and your review process from week one.
Project Based Engagement
Fixed scope, fixed timeline, agreed acceptance criteria. This suits a cluster build, a migration off legacy compute, or an inference cost reduction sprint.
Industries Where Our AI Infrastructure Engineers Deliver Impact
Our team serves global clients across healthcare, banking and financial services, manufacturing, retail, logistics, and technology. Our specialists build the compute, pipeline, and serving layers that keep AI workloads reliable under real load.
Chief Executive Officer
Chief Executive Officer
Looking to Hire AI Infrastructure Engineers Who Can Own Your Platform?
Book a Free ConsultationFrequently Asked Questions
What does an AI infrastructure engineer do?
An AI infrastructure engineer builds and runs the compute, storage, and deployment layer that machine learning workloads depend on. The work covers GPU provisioning, Kubernetes orchestration, training and data pipelines, model serving, CI/CD, monitoring, and cost control. They own the platform, while data scientists own the models.
What is the difference between an AI infrastructure engineer and an MLOps engineer?
The two roles overlap, and the split depends on team size. An AI infrastructure engineer works closer to the metal: GPU clusters, networking, Kubernetes, storage, and capacity planning. An MLOps engineer works closer to the model lifecycle: experiment tracking, model registries, retraining pipelines, and deployment workflows. Smaller teams often hire one person to cover both.
How much does it cost to hire AI infrastructure engineers?
Cost depends on seniority, location, and whether you need one engineer or a full platform pod. US market data puts AI infrastructure engineer pay at roughly $107,000 to $167,000 a year, and senior or HPC focused roles go higher. A dedicated engineer on a hybrid or offshore model costs less and can start faster. Entrans prices each engagement after a short scoping call.
Can AI infrastructure engineers reduce our GPU and inference costs?
Yes, and it is often the fastest return on the hire. Common wins include right sizing instances, mixing spot and reserved capacity, scaling idle clusters down, batching requests, and quantizing models for cheaper serving. Our engineers start by measuring actual utilization, because most clusters are provisioned for a peak that rarely arrives.
Can I hire remote AI infrastructure engineers who overlap with my time zone?
Yes. Entrans delivers through onshore, nearshore, and offshore teams across the US and India. You can hire remote AI infrastructure engineers with four or more hours of daily overlap, and on call coverage can be arranged where uptime matters. Overlap hours are agreed before onboarding, not after.
