ML Ops Engineer (EMEA Remote)

📋 Description

• Build and operate production-grade ML model serving infra (vLLM, TGI, Triton)
• Design deployment pipelines with blue/green and canary rollouts
• Develop auto-scaling and multi-model serving architectures
• Optimize GPU usage, memory, and network throughput for inference
• Design observability for latency, throughput, GPU usage, costs
• Manage model registries and CI/CD for automated deployments

🎯 Requirements

• 4+ years in ML Ops/Platform Eng/SRE for ML
• Hands-on with model-serving frameworks (vLLM, TGI, Triton)
• Container orchestration and production GPU workloads
• MLOps tooling: model registries, experiment tracking, automated pipelines
• Python and IaC (Terraform, Helm, or similar)
• Knowledge of distributed systems, performance tuning, reliability

🎁 Benefits

• Take ownership of infrastructure powering a rapidly scaling AI-native cloud platform
• Build foundational ML inference systems from the ground up
• Work at the intersection of distributed systems, GPU computing, sustainable cloud architecture
• Gain deep expertise in next-generation AI infrastructure and large-scale model serving systems
• Influence core engineering decisions and define best practices to scale with the company