AI Inference Platform Engineer

XpertDirect; Berlin; Vor Ort; EUR 70.000 - 110.000; Vollzeit

Zusammenfassung

XpertDirect in Berlin specializes in AI infrastructure, delivering scalable model serving on GPU-enabled platforms. The AI Inference Platform Engineer will build, optimise and operate the production inference stack across Kubernetes, vLLM, NVIDIA Triton, and cloud GPU environments.

You will focus on improving latency, throughput, and GPU utilisation while designing autoscaling, observability, and reliable deployment workflows.

Qualifikationen

• 4+ years in AI Infrastructure, ML Infrastructure, MLOps, Platform Engineering or similar roles.
• Strong understanding of Linux and distributed production systems.

Aufgaben

• 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

Kenntnisse

4+ years in AI Infrastructure, ML Infr

Linux & distributed systems

Tools

CUDA

NVIDIA GPU Operator

PyTorch

KServe

Ray Serve

LLM inference optimisation

Multi-GPU inference

AWS / GCP GPU infrastructure

Jobbeschreibung

AI Infrastructure | Model Serving | GPU Computing | Inference Engineering | ML Platforms

Our client, a growing AI Infrastructure company based in Berlin, is looking for an AI Inference Platform Engineer to build and optimise the platform used to serve production AI models across GPU-enabled infrastructure.

You'll work at the intersection of AI Infrastructure, Distributed Systems, and Platform Engineering, focusing on inference performance, GPU utilisation, autoscaling, latency, and reliability.

What You’ll Work On

• 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

Core Skills

• 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
• LLM inference optimisation
• Multi-GPU inference
• AWS / GCP GPU infrastructure
• Experience operating high-throughput or latency-sensitive inference services