platform engineer for AI infrastructure

The company provides AI infrastructure for large-scale model training and high-volume inference.

• Build and operate GPU-enabled Kubernetes infrastructure;
• Support distributed model training and large-scale inference workloads;
• Optimise GPU scheduling, allocation, utilisation, and performance;
• Build distributed compute environments using Ray;
• Develop platform tooling and automation in Python;
• Provision infrastructure using Terraform;
• Improve observability across GPU, Kubernetes, and workload layers;
• Troubleshoot performance bottlenecks across compute, memory, networking, and storage;
• Build self-service capabilities for ML and AI engineering teams;
• Improve platform reliability, scalability, and compute efficiency.

• 4+ Years in ML Infrastructure, Platform Engineering, MLOps, HPC, or similar roles;
• Kubernetes;
• NVIDIA GPU infrastructure;
• CUDA ecosystem;
• Python;
• Terraform;
• Ray or comparable distributed compute technologies;
• Monitoring and observability;
• Strong understanding of Linux and distributed systems;
• Nice to have: NVIDIA GPU Operator, NCCL and distributed GPU communication, PyTorch distributed training, Triton Inference Server / vLLM, Prometheus / Grafana / OpenTelemetry, AWS, GCP, or Azure GPU infrastructure, Slurm or HPC environments, GPU capacity planning and cost optimisation, large-scale LLM training or inference.

• Remote work across the European Union.