AI Infrastructure Engineer (GPU) - Remote EMEA

The role

You will design and operate core infrastructure that serves machine learning models at scale. This role focuses on building scalable, reliable, and efficient ML inference platforms for real-time AI applications.

What you will do

• Build and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent
• Design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models
• Develop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers
• Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance
• Design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health
• Manage model registries and CI/CD pipelines enabling automated and reproducible model deployments
• Own the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities
• Define engineering best practices and contribute to platform scalability in a fast-moving startup environment

Required qualifications

• 4+ years of experience in ML Ops, Platform Engineering, SRE, or similar infrastructure roles focused on ML systems
• Hands-on experience with model serving frameworks such as vLLM, TGI, Triton, or equivalent
• Strong background in container orchestration and operating GPU-based workloads in production
• Experience with MLOps tooling including model registries, experiment tracking, and automated deployment pipelines
• Proficiency in Python and infrastructure-as-code tools (e.g., Terraform, Helm, or similar)
• Strong understanding of distributed systems, performance tuning, and production reliability engineering
• Ability to effectively use AI coding assistants to accelerate development and debugging workflows
• Ownership mindset with the ability to operate independently in a remote-first environment
• Fluent English required

Nice to have

• Experience with ML platforms such as Kubeflow, MLflow, or KubeAI
• Knowledge of GPU scheduling, CUDA/ROCm optimization, or multi-tenant inference systems
• Experience with cost optimization across different GPU types and inference workloads
• Background in early-stage startups or greenfield infrastructure projects
• Proven experience building production systems from scratch rather than maintaining legacy platforms

About the company

Pragmatike is recruiting on behalf of a fast-scaling, well-funded distributed cloud infrastructure startup building next-generation AI-native cloud services.