ML Ops Engineer (EMEA Remote)

Overview

Build and operate scalable, reliable ML inference infrastructure for real-time AI applications in a remote ML Ops Engineer role.

What you'll do

• Build and operate production-grade model-serving infrastructure.
• Implement blue/green and canary deployment pipelines for ML models.
• Develop auto-scaling, multi-model serving, and request-routing systems.
• Optimize GPU utilization, memory, network throughput, and artifact storage.
• Design observability for inference latency, throughput, GPU usage, cost, and system health.
• Manage model registries and CI/CD pipelines for reproducible deployments.
• Own ML system operations, including production support and on-call responsibilities.

What you'll need

• 4+ years in ML Ops, Platform Engineering, SRE, or similar ML infrastructure roles.
• Hands-on experience with vLLM, TGI, Triton, or equivalent model-serving frameworks.
• 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 reliability engineering.
• 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.
• 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

• Location: Ukraine.
• Work mode: Fully remote in the EMEA timezone.
• Fluent English required.
• Start date: ASAP.