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Beginner100% tuition-freeDuration: 12 months4 Certification preparation

Full Stack AI & Cloud Engineering Program

One year from workplace AI basics to production GenAI engineering on AWS. You learn governance, private LLMs, and build multiple capstones for the German job market.

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Twelve months full-time (~2,400 teaching units), our flagship program: four blocks from AI and automation through full-stack software and AI systems to AWS GenAI, MLOps, and private LLMs. Governance (GDPR, EU AI Act) from day one. Multiple capstones across the year. Google AI and AWS certification prep. For career changers with no tech background targeting AI or cloud engineering.

What you'll be able to do

  • Governance-first AI from day one (GDPR, EU AI Act)
  • Deployable full-stack software with CI/CD and containers
  • Integration, RAG, agents, evaluation, and operations
  • AWS GenAI, MLOps/LLMOps, and cost discipline
  • Private/on-prem LLM literacy (data residency)
  • Prep for Google AI, AWS Cloud & AWS AI Practitioner

Portfolio proof

  • Multiple capstone projects across the year
  • Production-ready GenAI deployment

Curriculum overview

4 modules

Module 1Months 1–3

AI & automation

Workplace AI and automation

n8nAPIsGoogle AI cert prep
Module 2Months 4–6

Full-stack

Web apps, Docker, CI/CD

ReactNode.jsAWS CP prep
Module 3Months 7–9

AI systems

RAG, agents, and reliability

PythonFastAPILangChainvector DB
Module 4Months 10–12

Cloud GenAI

AWS GenAI, MLOps, and governance

AWS GenAIOllamavLLM

Frequently asked questions

Common questions about this program — funding, format, and outcomes.

Who it's for

Yes. The full 12-month pathway starts from the foundations and does not require previous programming experience at entry. However, it is an intensive full-time program, so you need motivation, consistency, digital confidence, and willingness to work on projects every week.

It is both. The program teaches software engineering through an AI-first approach. You learn to build real applications and systems while using AI tools responsibly and verifying the quality of AI-assisted work.

Curriculum & format

Because the goal is not only to introduce AI tools, but to build a complete path from workplace AI and automation to software development, AI systems, cloud deployment, GenAI engineering, governance, and portfolio-ready capstones.

You will learn practical AI use, automation, APIs, JavaScript/TypeScript, React, backend development, databases, authentication, Docker, CI/CD, AWS cloud basics, AI systems, RAG, agents, evaluation, monitoring, local/private LLM concepts, and cloud GenAI engineering.

The main stack includes JavaScript/TypeScript, React, Node.js, databases such as PostgreSQL or Supabase, Docker, GitHub Actions, AWS, n8n, AI APIs, and selected AI engineering tools. Later stages may include Python, FastAPI, LangChain or similar tools, vector databases, Ollama, vLLM, Bedrock, and SageMaker concepts.

Yes. Cloud concepts are introduced progressively, with AWS as the main cloud direction. You will learn cloud fundamentals, deployment thinking, observability, cost awareness, security basics, and later cloud GenAI architecture patterns.

Yes. The program is project-based. Each block includes practical artifacts and a capstone. By the end, you should have a portfolio that includes workflow automations, applications, AI system prototypes, deployment evidence, documentation, and a final cloud GenAI project.

The program includes Ai School / StartSteps completion evidence and structured preparation for external certifications such as Google AI Professional Certificate, AWS Certified Cloud Practitioner, and AWS Certified AI Practitioner. Advanced AWS GenAI or Machine Learning certifications may be treated as optional stretch goals depending on readiness and cohort plan.

The full program is designed as an intensive full-time learning path (~2,400 teaching units over 12 months). You should expect live sessions, guided labs, project work, self-study, certification preparation, and portfolio development across the week.

Struggling is normal in an intensive technical program. Support may include instructor feedback, one-on-one mentoring, review sessions, peer learning, project guidance, and additional practice recommendations. The key is to communicate early and work consistently.

In Germany and the EU, AI projects must consider privacy, accountability, security, and responsible use. We teach these topics from the beginning so students can build solutions that are not only impressive, but also realistic for workplaces.

Outcomes & funding

Depending on your progress and prior background, the program can prepare you for roles such as junior full-stack developer, AI-assisted software developer, automation or integration engineer, AI systems developer, junior GenAI application engineer, or cloud-oriented AI project roles.

The program is designed to build job-relevant skills and employer-checkable project evidence. Your final readiness depends on your effort, project quality, language level, prior experience, and the roles you apply for. We help you package your work into a clear portfolio and career story.

This program may be fundable through a Bildungsgutschein if you are eligible. You will need to make an appointment with your local Agentur für Arbeit or Jobcenter — they decide individually. We can share course information and help you prepare for that conversation.