AI Enablement Engineer

Role : AI Enablement Engineer Location : Stockholm, Sweden Employment Type : Employment Payroll Contract Duration : 12+ Months Key responsibilities:

• Deploy and configure VMware Private AI Foundation with NVIDIA and NVIDIA AI Enterprise components, including NVIDIA NIM microservices and NeMo Guardrails.
• Configure GPU sharing (MIG and/or vGPU time-slicing) so GPUs are used efficiently across workloads.
• Expose GPU utilisation and LLM workload metrics to Prometheus (for example via the DCGM exporter), with dashboards.
• Validate that the client's AI components run correctly on the Kubernetes (VKS) platform.
• Run scalability tests for GPU and storage clusters and document the results.

Must-have:

• NVIDIA AI Enterprise on Kubernetes, including the NVIDIA GPU Operator and NIM deployment.
• GPU sharing with MIG and/or vGPU time-slicing.
• Kubernetes in production, including Helm.
• GPU and workload monitoring with Prometheus and Grafana (including DCGM exporter).
• Hands-on LLM inference serving and performance testing (for example NIM, Triton or vLLM).

Nice to have:

• VMware Private AI Foundation with NVIDIA
• NeMo Guardrails
• Vector databases (for example pgvector)
• VKS or vSphere with Tanzu
• NVIDIA AI infrastructure certification, CKA or CKAD

Not a fit:

• Data scientists or ML researchers focused on model development rather than infrastructure
• Kubernetes engineers without GPU experience