Senior Platform Engineer (f/m/x)

Workist Gmbh; Berlin; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Benefits dieser Stelle

Office in city center

Table tennis & darts

Flexible working hours

Office & Remote options

Zusammenfassung

Workist Gmbh in Berlin seeks a Senior Platform Engineer who owns the platform used by our AI agents. You will manage two clouds, AKS clusters for production and ML workloads, and the path from merge request to production across all services.

You will shape platform evolution, keep it secure, observable and cost-efficient, and extend it as our product grows; recent work includes LLM gateway with region failover and GPU capacity planning.

Qualifikationen

• Production infrastructure ownership experience.

Aufgaben

• Own the platform and its architecture across Azure and AWS; ensure isolation and scalability.
• Design and deliver platform capabilities for GPU capacity, LLM gateway and new environments.
• Set the standard for Kubernetes operations and managed data services with proactive capacity planning.
• Maintain security posture end-to-end: least-privilege, network isolation, scanner findings, patch cadence.
• Own observability and alerting; lead incident response and post-mortem fixes.
• Run quarterly backup/restore and disaster-recovery tests; act on findings.
• Improve deployment velocity from merge request to production with reliable environments and access on demand.

Kenntnisse

Kubernetes

Terraform

Azure

AWS

Python

GitOps

CI/CD

Security

Observability

Cost optimization

Tools

Azure AKS

Helm

CI/CD pipelines

GitLab/GitHub

Jobbeschreibung

Your role with us

As Senior Platform Engineer , you own the platform that Workist's AI agents run on: two clouds, the Kubernetes clusters for production, staging and ML workloads, and the path from merge request to production for every service we ship. You decide how this platform evolves, you keep it secure, observable and cost-efficient, and you extend it as the product grows. Most recently that meant an LLM gateway with region failover in front of our Azure OpenAI deployments, and performing GPU capacity planning for our own models.

How we build and run things

Everything runs on Kubernetes (Azure AKS), deployed with Helm and GitOps/Flux. All infrastructure is Terraform, applied through CI.

CI/CD for every service on GitLab

Managed services wherever they keep life simple: Postgres, OpenSearch, Redis, blob storage. We self-host only where it clearly pays off.

Two clouds: Azure as our primary cloud, AWS for search and mail ingestion.

Python everywhere: read and fix application code when that is where the fix belongs.

Security is routine: automated scanning in every pipeline, regular external pentests, quarterly backup and disaster-recovery tests.

Your responsibilities

• Own the platform
• Own the architecture of our cloud environments across Azure and AWS: how subscriptions, networks, identities and environments are structured and stay isolated
• Design and deliver the platform capabilities the product needs next, from GPU capacity and an LLM gateway to new environments
• Set the standard for how we run Kubernetes and our managed data services: capacity planned ahead of growth, changes and upgrades rolled out safely and routinely, costs kept in check
• Keep it quiet Own the security posture end to end: least-privilege access, network isolation, findings from scanners and pentests, and a patch cadence that runs itself. Be a technical counterpart for audits and security questionnaires
• Own observability and alerting so that every alert is worth acting on, and lead the response to platform incidents including the follow-up fixes
• Run quarterly backup/restore and disaster-recovery tests, and close what they reveal
• Make the team faster Own the path from merge request to production: fast builds, reliable deployments, environments and access on demand
• Build the infrastructure behind our AI models: the LLM gateway, model deployments with region failover, GPU capacity, and whatever the next model needs

What this looked like recently

a Redis instance that silently dropped half of its new TCP connections and took the Celery workers down with it, region failover and load balancing for our Azure OpenAI deployments, moving our clusters from nginx ingress to the Kubernetes Gateway API.

Your profile

We're looking for a professional team player who thrives in an environment where we share knowledge openly and push each other to deliver the best possible outcome. What you bring:

• You have run production infrastructure for years , ideally as the person who owned it end to end rather than one of many in a large ops team
• Kubernetes and Terraform in production : cluster upgrades, Helm, GitOps, and infrastructure as code for a whole cloud estate
• Production experience on Azure or AWS . Most of our estate is Azure, so an AWS background works if you are willing to go deep on Azure.
• Active Python skills : you read and debug a Python codebase and fix the application when the application is what is broken
• Security as a habit : you have run patch management, handled pentest findings and designed least-privilege access, and you prioritise findings pragmatically
• You write clearly : runbooks, incident write-ups and architecture decisions that others can follow
• Fluent English (engineering runs in English, so German is not required, though it can help in some customer and vendor conversations)

Our benefits & culture

• Worki Tower: Our office in the heart of the city over 8 floors invites you to work professionally at an ergonomic workplace and gives you space for your creativity. Our own Workist Lounge awaits you with a cozy atmosphere including coffee, drinks and a vitamin basket for short or longer creative breaks.
• Play table tennis, darts or Nintendo to clear your head and exchange ideas with other teams.
• Flexible working hours: Shape your working day with our flexible working hours. Have important appointments in the morning or evening? No problem, take advantage of work schedule flexibility.
• Office, Remote & Workation: You decide yourself and have the flexible choice between an office or remote f