Platform Engineer

Nostrion; Leusden; On-site; EUR 70,000 - 110,000; Full time

Job summary

Nostrion is building independent European AI platforms for regulated environments. As a Platform Engineer, you will design, deploy, and run the infrastructure behind our AI applications and customer environments, uniting GPU resources, Kubernetes, and scalable model infrastructure.

You will automate releases, optimize models for production, and ensure secure, observable, and cost-aware operation across the stack. Join a hands-on team shaping sovereign European AI for professionals.

Qualifications

• Experience as a Platform Engineer, Infrastructure Engineer, DevOps Engineer, SRE, or similar role.
• Strong knowledge of production Kubernetes, including deployments, scheduling, networking, storage, and access control.
• Hands-on experience with NVIDIA GPUs in Linux and container environments.
• Experience running or supporting AI/LLM workloads using tools such as vLLM, NVIDIA Triton, TensorRT-LLM, or similar.
• Experience with Infrastructure as Code (Terraform or OpenTofu) and deployment tooling such as Helm.
• Knowledge of CI/CD, ideally GitOps with Argo CD or Flux.
• Experience monitoring and troubleshooting distributed systems.
• Scripting or programming skills in Python, Bash, or Go.
• Understanding of network security, secrets management, and isolation between customer environments.

Responsibilities

• Build, operate, and improve Kubernetes environments for AI workloads and customer applications.
• Make NVIDIA GPUs available to workloads and provide visibility into GPU utilization and capacity.
• Automate AI and LLM deployments, including configuration, versioning, and controlled updates.
• Configure and optimise model-serving environments for latency, throughput, and efficient GPU memory usage.
• Provision customer environments with appropriate isolation of workloads, data, network traffic, and access.
• Develop Infrastructure as Code and reusable configurations for consistent environments.
• Build and improve CI/CD and GitOps processes for platform components and model deployments.
• Implement monitoring, logging, and alerting for infrastructure, GPUs, model endpoints, and customer environments.
• Improve availability, backups, recovery procedures, and capacity planning.
• Investigate incidents, resolve root causes, and implement lasting improvements.
• Work with AI and software engineers to turn experiments into stable production services.
• Document technical decisions, configurations, and operational procedures clearly.

Skills

Platform Engineer

Kubernetes

NVIDIA GPUs

AI/LLM workloads

GitOps

CI/CD

Python

Bash

Go

Security & secrets management

Tools

Terraform

OpenTofu

Helm

Argo CD

Job description

Nostrion builds independent European AI for organisations that work with sensitive information and operate in regulated sectors. We help professionals put their organisation’s knowledge to work while retaining control over their data, processes, and expertise.

European ownership, infrastructure, and governance form the foundation of our approach. We combine open models with our own platform and work alongside domain experts to develop applications that fit their profession’s daily needs.

Fidacta, our solution for the notarial sector, makes this tangible. It helps notaries and their teams search documents, obtain answers grounded in their own information, generate draft documents, and streamline workflows. These applications depend on a platform that handles sensitive customer data and AI workloads securely and reliably.

Your Role

As a Platform Engineer, you build and operate the technical environment behind our AI applications and customer environments. You bring together NVIDIA GPU capacity, Kubernetes, and AI model infrastructure to create a reliable, secure, and manageable platform.

You deploy models, provision customer environments, and automate releases. Your work spans the full stack of infrastructure: GPU resources, networking, storage, model serving, monitoring, and access management.

You take responsibility for how models run in production: their performance under load, their observability, and how they can be updated safely. You investigate bottlenecks, balance performance against capacity and cost, and help engineers bring AI applications into production with confidence.

What You’ll Be Doing

• Build, operate, and improve Kubernetes environments for AI workloads and customer applications.
• Make NVIDIA GPUs available to workloads and provide visibility into GPU utilisation and capacity.
• Automate AI and LLM deployments, including configuration, versioning, and controlled updates.
• Configure and optimise model-serving environments for latency, throughput, and efficient GPU memory usage.
• Provision customer environments with appropriate isolation of workloads, data, network traffic, and access.
• Develop Infrastructure as Code and reusable configurations for consistent, reproducible environments.
• Build and improve CI/CD and GitOps processes for platform components and model deployments.
• Implement monitoring, logging, and alerting for infrastructure, GPUs, model endpoints, and customer environments.
• Improve availability, backups, recovery procedures, and capacity planning.
• Investigate incidents, resolve root causes, and implement lasting improvements.
• Work with AI and software engineers to turn experiments into stable production services.
• Document technical decisions, configurations, and operational procedures clearly.

What You Bring

You are a practical engineer who enjoys understanding how systems work and takes responsibility for their reliability. You can build and operate infrastructure while keeping the needs of developers and users in mind.

• Experience as a Platform Engineer, Infrastructure Engineer, DevOps Engineer, SRE, or in a similar role.
• Strong knowledge of production Kubernetes, including deployments, scheduling, networking, storage, and access control.
• Hands-on experience with NVIDIA GPUs in Linux and container environments.
• Experience running or supporting AI and LLM workloads, using tools such as vLLM, NVIDIA Triton, TensorRT-LLM, or similar technologies.
• Experience with Infrastructure as Code, such as Terraform or OpenTofu, and deployment tooling such as Helm.
• Knowledge of CI/CD, ideally including GitOps with tools such as Argo CD or Flux.
• Experience monitoring and troubleshooting distributed systems.
• Scripting or programming skills in languages such as Python, Bash, or Go.
• An understanding of network security, secrets management, and isolation between customer environments.
• A careful, independent approach and the ability to explain technical trade-offs clearly.

Bonus Points If You Have

• Experience with NVIDIA GPU Operator, NVIDIA Container Toolkit, and GPU monitoring with DCGM.
• Knowledge of GPU sharing, MIG, multi-GPU inference, and GPU workload scheduling.
• Experience optimising LLM inference through batching, quantisation, or KV-cache management.
• Experience with private cloud, on-premises infrastructure, or environments with restricted external connectivity.
• Familiarity with model registries, artifact storage, and reproducible model releases.
• Experience improving networking and storage performance for large models and datasets.
• Experience in secure or regulated environments where changes and access must be demonstrably controlled.

Your Team

You work closely with AI Engineers, Software Engineers, Product, and colleagues supporting customer implementations. Together, you ensure that new models and applications become available safely and reliably.

You help engineers work independently through clear deployment processes, useful monitoring, and dependable platform services. You use lessons from production to improve both the infrastructure and the developer experience.

What We Offer

• Ownership of the infrastructure behind our AI applications and customer environments.
• A hands-on technical role working with NVIDIA GPUs, Kubernetes, and modern AI and LLM technologies.
• The opportunity to shape architecture, automation, and platform standards.
• Direct collaboration with the engineers developing models and applications.
• A competitive salary and attractive employment conditions.
• The opportunity to help build sovereign European AI for professional use.