Key Skills

Job Description

Great AI doesn’t start with a great model. It starts with a great platform. The success of an AI initiative is rarely determined by the model alone. More often, it depends on the quality of the platform underneath it. As a Platform Engineer in our central Data & AI team, you’ll build the foundations that let engineers develop, deploy and operate AI workloads safely and efficiently. You’ll work on the infrastructure, automation and platform capabilities that allow teams to innovate without compromising reliability, and you’ll bridge the gap between promising technology and production reality. Think Kubernetes platforms, deployment automation, observability, security and developer experience. Think AI workloads built around Large Language Models, retrieval systems, graph technologies and modern data platforms. Think creating an environment where engineers focus on solving problems rather than fighting infrastructure. What you will work on Designing self-service platform capabilities using Kubernetes, Terraform and GitOps principles Operationalizing graph and vector databases, and enabling model serving workloads Improving the observability of AI systems running in production Integrating OpenAI or Anthropic-based services into our platform Operationalizing LangGraph workloads on Kubernetes Enabling GraphRAG architectures powered by Neo4j Designing deployment patterns so AI services meet the same standards as any other production workload You’ll spend less time tuning models and more time making sure AI capabilities can be trusted, operated and continuously improved. This role sits at the intersection of platform engineering, software engineering and AI. At Schuberg Philis we don’t believe in throwing solutions over the fence. The engineers building systems are responsible for running them too, and that ownership extends to AI just as much as every other technology we work with. Your impact won’t be measured by the number of clusters you manage, but by how much autonomy and confidence other engineers gain from the platform you’ve built. We’d love to meet you if You care deeply about building platforms that other engineers love to use, and you’re happiest when automation replaces repetitive work You have a strong background in Platform Engineering, SRE, infrastructure or distributed systems You have extensive experience building and operating Kubernetes-based and cloud-native environments, with infrastructure automation at scale You’ve built and operated production environments using tools such as Terraform, ArgoCD, GitHub Actions or similar You understand what it takes to operationalize AI workloads in production, and technologies such as OpenAI, Anthropic, Neo4j, GraphRAG, vector databases or LangGraph are familiar territory You believe observability, reliability and security belong in every design conversation You enjoy improving developer experience through automation and self-service You see the gap between a successful proof of concept and a production-ready system and immediately start figuring out how to close it You take ownership of the result You get satisfaction from enabling other engineers to move faster and safer You are currently based in the Netherlands. Questions? Please do not hesitate to contact Maggie: 0623757086 Only applications submitted through the website will be considered. Please note that we do not work with external recruitment agencies and we will not pay any fees for unrequested CV introductions.

Core Responsibilities

Design and operate self-service Kubernetes platform capabilities, infrastructure automation, and deployment patterns for production AI workloads. Improve observability, reliability, security, and developer experience while enabling model serving, graph and vector databases, LangGraph, and GraphRAG architectures.

Requirements

Candidates should have a strong background in platform engineering, SRE, infrastructure, or distributed systems, with extensive experience operating Kubernetes-based cloud-native environments and automating infrastructure at scale. They should understand production AI operations and be familiar with tools and technologies such as Terraform, ArgoCD, GitHub Actions, OpenAI or Anthropic services, Neo4j, GraphRAG, vector databases, and LangGraph; candidates must be based in the Netherlands.