Senior AI Platform Engineer; m​/f​/d

For mid-sized companies, knowledge is the last real competitive lever. Yet knowledge gets stuck in tools and SharePoint folders — or disappears when experts leave. While IT is still planning, employees are already using ChatGPT and the like — without governance, and sensitive data is leaking out.

With the Knowledge Bots platform, Blockbrain creates what companies truly need: AI-powered knowledge management that is quick to implement, flexibly scales, and operates in a compliant manner. Teams use our GenAI building blocks to build tailored AI assistants, agents, and workflows in minutes — just like Lego.

Series A funded with strong growth momentum (10x product usage & 5x revenue in 2025)

Enterprise clients such as Roland Berger, Bosch, IONOS , and Harting from industries including manufacturing, finance, and legal — sectors with the highest security requirements

ISO 27001 certified, EU AI Act ready. Made in Germany.

You’ll join Blockbrain’s
Frontier Team
, a very small team of
four people
that acts as our innovation hub.
The Frontier
is the edge of the map for exploration: a place where we scout the AI landscape, prototype aggressively, and validate market opportunities before competitors do.

You turn what the AI team builds into production-ready systems. You step in where architecture, scalability, security, or operations become real constraints, and make sure only what can withstand enterprise load and audit requirements reaches production.

Working in the Frontier Team means operating at the newest edge of AI ’ll be close to emerging technologies, fast-moving ideas, and early technical validation, while still making sure the outcome is solid enough for real enterprise use.

You work closely with AI Engineering, Product, and Engineering to evaluate technical options, define the right infrastructure context, and build the platform patterns that let teams move fast without compromising reliability or compliance.

Review and harden AI systems:Evaluate new AI components, prototypes, and tooling proposals, and turn them into production-ready systems.

Make architecture decisions:Define the right model serving setup, deployment patterns, workload isolation, and abstraction layers.

Own our platform footprint:Run and evolve our multi-cloud and on-prem environments across AWS, GCP, Azure, and customer-hosted setups.

Operate the Kubernetes stack:Work with Kubernetes, ArgoCD, Terraform, Git Hub Actions, Prometheus, and Grafana to keep the platform scalable and observable.

Build secure runtime patterns:Create sandbox, runtime, and isolation patterns that help AI engineers move fast without breaking compliance.

Embed Dev Sec Ops  practices:Bring threat modeling, secure SDLC, secrets management, and supply-chain security into the delivery process.

Enable LLM infrastructure:Support model serving, vector stores, RAG systems, inference scaling, GPU orchestration, and observability.

Extend core services when needed:Read, review, and extend Type Script microservices and APIs to secure, scale, or unblock technical work.

Strong understanding of LLM systems:You know how inference pipelines work and where they can fail in production.

AI infrastructure fluency:You are comfortable with model serving, RAG, embeddings, vector databases, and evaluation tooling.

AI as a daily co-pilot:You use AI tools to improve Dev Ops workflows, automate infrastructure work, and sharpen your technical output.

We are not looking for AI researchers. We are looking for engineers who use AI as a serious technical multiplier.

7+ years of experience
in platform, infrastructure, SRE, or Dev Sec Ops  roles, ideally with exposure to AI or ML systems in production.

Hands-on depth
with Kubernetes, IaC, CI/CD,…