AI Infrastructure Engineer (GPU) - Remote EMEA

Overview

Build and operate scalable, reliable, and cost-efficient ML inference infrastructure for real-time AI applications in a distributed cloud environment.

What you'll do

• Build and operate production-grade model-serving infrastructure using vLLM, TGI, Triton, or equivalent.
• Implement blue/green and canary deployment pipelines for ML models.
• Develop auto-scaling, multi-model serving, and intelligent request-routing systems.
• Optimize GPU utilization, memory efficiency, network throughput, and model storage performance.
• Design observability for inference latency, throughput, GPU usage, cost, and system health.
• Manage model registries and automated CI/CD model deployments.
• Support the full ML system lifecycle, including production operations and on-call responsibilities.

What you'll need

• 4+ years of experience in MLOps, platform engineering, SRE, or similar ML infrastructure roles.
• Hands-on experience with model-serving frameworks such as vLLM, TGI, or Triton.
• Experience operating containerized GPU workloads in production.
• Experience with model registries, experiment tracking, and automated deployment pipelines.
• Proficiency in Python and infrastructure-as-code tools such as Terraform or Helm.
• Understanding of distributed systems, performance tuning, and production reliability engineering.
• Fluent English and ability to work independently in a remote-first environment.

Nice to have

• Experience with Kubeflow, MLflow, or KubeAI.
• Knowledge of GPU scheduling, CUDA/ROCm optimization, or multi-tenant inference systems.
• Experience optimizing costs across GPU types and inference workloads.
• Background in early-stage startups or greenfield infrastructure projects.
• Experience building production systems from scratch.

Details

• Fully remote in the EMEA timezone.
• Start date: ASAP.