platform engineer for AI infrastructure

The company develops AI infrastructure for serving production AI models across GPU-enabled infrastructure, focusing on distributed systems, platform engineering, inference performance, GPU utilisation, autoscaling, latency, and reliability.

• Build and operate Kubernetes infrastructure for production AI inference;
• Deploy and optimise model-serving workloads using vLLM and NVIDIA Triton;
• Improve GPU utilisation, throughput, and inference latency;
• Design autoscaling strategies for dynamic AI workloads;
• Build platform tooling and automation in Python;
• Provision and manage infrastructure using Terraform;
• Develop observability across models, GPUs, Kubernetes, and serving infrastructure;
• Profile and troubleshoot inference performance bottlenecks;
• Improve batching, concurrency, caching, and resource allocation strategies;
• Build reliable deployment workflows for new models and model versions;
• Partner with ML Engineers to move models efficiently into production.

• 4+ Years in AI Infrastructure, ML Infrastructure, MLOps, Platform Engineering, or similar roles;
• Strong understanding of Linux and distributed production systems;
• Nice to have: CUDA, NVIDIA GPU Operator, PyTorch, KServe, Ray Serve, Prometheus / Grafana / OpenTelemetry, LLM inference optimisation, quantisation techniques, multi-GPU inference, AWS / GCP GPU infrastructure, experience operating high-throughput or latency-sensitive inference services.

• Hybrid role in Berlin, Germany.