20 beste Stellen als cloud engineer in germering, by – aktuell auf Personalsuche

Backend Engineer (Infrastructure & Platform) (f/m/d)
Zeit AI (YC S24) —München
- 95.000 € – 270.000 € pro Jahr
- Vollzeit
- Empfehlungsprogramm
Direkt bewerben
2 Tage
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Avelios Medical GmbH —München
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Impower GmbH —München
- Vollzeit
- Azure
- Java
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BMW —München
- Vollzeit
- Azure
- Informatik
- Java
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Summitas Gruppe GmbH —München
- Vollzeit
- Azure
- Englisch
- Mit Reiseanteil
- Aufstiegsmöglichkeiten
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12 Tage
Working Student DevOps Engineer (all genders)
Avelios Medical GmbH —München
- Teilzeit
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(Senior) AI Platform Engineer (m/w/d)
OMMAX GmbH —München
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Senior Cloud Platform Engineer
Pentland Firth Software GmbH —München
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PAYBACK GmbH —München
- Bereitschaftsdienst
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AWS AI & DATA ENGINEER (M/W/D)
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- Hybrides Arbeiten
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Senior Cloud (Data) Platform Engineer (m/w/d)
Deloitte Consulting GmbH —München
- Teilzeit
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- Englisch
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Senior DevOps Engineer (m/w/d)
Collaboration Betters The World GmbH —München
- 75.000 € – 95.000 € pro Jahr
- Hybrides Arbeiten
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Direkt bewerben
10 Tage
Titel Principal DevOps Engineer (all genders)
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SAP DevOps Engineer (f/m/d)
E.ON Digital Technology GmbH —München
- Vollzeit
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Working Student DevOps Engineer (all genders)
Avelios Medical —München
- Teilzeit
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Teamlead DevOps
ALL IN GROUP —München
- Vollzeit
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ALTEN —München
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Working Student CodeCraft Software Development Infrastructure (f/m/x)
BMW —München
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Software Engineer & IoT Consultant (all genders)
Accenture —München
- Vollzeit
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Lead Engineer - Service Platform (f/m/d)
Tangany GmbH —München
- Hybrides Arbeiten
- Azure
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- Softwareentwicklung
- Homeoffice-Möglichkeit
- Flexible Arbeitszeiten
Direkt bewerben

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cloud engineer jobs near germering, by

Backend Engineer (Infrastructure & Platform) (f/m/d)

Zeit AI (YC S24)
München

Direkt bewerben

Details zur Stelle

Vollzeit
95.000 € – 270.000 € pro Jahr
vor 2 Tagen

Leistungen

Empfehlungsprogramm

Qualifikationen

Cloud-Infrastruktur
Docker
PostgreSQL
TypeScript
VPN

Vollständige Stellenbeschreibung

The opportunity

LLMs are changing analytical work. Capabilities that once required large teams of highly paid data engineers are becoming accessible to smaller companies for the first time. At Palantir, we delivered real data and BI value, but deployments never scaled without expensive, hands-on engineers. We believe LLMs change that, and we have the customers and revenue to prove the model works.

Now we scale and one key lever is the platform. Every new customer brings new data sources, more rows to sync, and more queries to serve. Your job is to make sure ZeitMind, our agent platform, can handle onboarding 10 new enterprise customers per week and the hard part isn't the compute. It's capturing each business's context fast enough: connecting messy data systems, making sure the agent's answers are correct, that visualisations hold up, and that the customer is able to get value out of the product quickly. This is as much a product and correctness problem as an infrastructure one, and it hasn't been solved before. Onboarding a customer should be boring. This is the role that lets everything else scale.

What you will do

Build the sync layer: millions of rows from ERP, CRM, and homegrown systems, ingested incrementally and reliably, without an engineer babysitting the pipeline
Cut onboarding time: connecting a new customer's data sources should take hours, not weeks. You abstract sources so our agents work with any of them the same way
Make the agent fast where it counts: speed comes from tool design that parallelizes, sub-agents, and branching, not tokens per second. You design tools so work can run concurrently and safely
Route data safely between customer networks and ours: security and reliability are features our customers pay for
Build the guardrails for correctness: automatic checks and integrated validation tooling so the agent's output can be trusted, and so it flags what a human should verify
Keep the platform simple: choose boring technology where boring wins, and be able to say why every system we run earns its place

You will thrive here if you

have built or scaled data platforms before and think clearly about data processing architectures
have a deep understanding of OLAP and OLTP systems and when to reach for each
bring strong backend experience with TypeScript and are at home in cloud infrastructure
get foundations right without overengineering them; you build for the scale we will hit next year, not for a hypothetical one
take full ownership from idea to production to impact, and are comfortable working without predefined specs
want to build foundations early rather than optimize mature systems
are genuinely interested in the data and agentic space and how LLMs enable new workflows for non-technical users

Requirements

You've been a lead architect or equivalent: built many systems yourself, and seen how large systems fail and evolve. You're here to learn from customers and take bets on a product that doesn't exist yet, not to learn how to write software.
Strong TypeScript and cloud infrastructure experience
English C1 or above; German a plus

What we offer

We are based in Munich and build the team around in-person work. We pay for your relocation.
Regular San Francisco offsites for product sprints and staying close to the frontier.
Zeit AI package: daily lunch allowance, free in-office dinner, wellpass membership, best tech & tools
You know someone? €5k referral bonus for every successful hire.

Tech stack

TypeScript end to end. Backend on Bun with Postgres and Supabase. React frontend (Mantine, TRPC, TanStack Query). Job/Process management, Docker, VPN/routing and Agentic frameworks: harness, tool calls, sandboxed tools, branching of data and specs.

About the interview process

First call with Elisa
Tech screening
Tech interview with Jonas
Decomposition interview
Second technical interview
Behavioural interview
Take-home case + Meet the team
References, offer

Apply

Still apply if you do not match every requirement. If you are exceptional in core areas and learn fast, we want to talk.

Compensation Range: €95K - €270K

Direkt bewerben

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