<blockquote> <p>Great AI doesn’t start with a great model.
It starts with a great platform.
</p> </blockquote> <p>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.
</p> <p>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.
</p> <p>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.
</p> <p><strong>What you will work on</strong></p> <ul> <li>Designing self-service platform capabilities using Kubernetes, Terraform and GitOps principles</li> <li>Operationalizing graph and vector databases, and enabling model serving workloads</li> <li>Improving the observability of AI systems running in production</li> <li>Integrating OpenAI or Anthropic-based services into our platform</li> <li>Operationalizing LangGraph workloads on Kubernetes</li> <li>Enabling GraphRAG architectures powered by Neo4j</li> <li>Designing deployment patterns so AI services meet the same standards as any other production workload</li> </ul> <p>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.
</p> <p>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.