Data Engineer (f/m/d) - AI
smartclip
About the role
About
We are not just another company feeding its data into someone else's black box. We are building our own AI stack that is scalable, secure, and 100% under our control. If you are tired of patching together pipelines nobody truly understands or maintaining old ETL monsters, read on. As a Data Engineer - AI, you will take ownership of the architecture and operation of our AI and Machine Learning applications — from raw data to production-ready models and services. You will work side-by-side with Data Scientists and ML Engineers to make models truly usable and drive the technical backbone of products running at scale.
- Real engineering: You don't just glue APIs together — you build systems.
- Visible impact: Your work powers live services — used, tested, and valued.
- Tech-first environment: We are AI and Data-focused from the ground up, not just in marketing.
Responsibilities
- Design, Build & Run: Architect scalable data backends for our AI and ML-driven products.
- Bridge the gap: Collaborate closely with Data Scientists to bring ML prototypes into production.
- Automate & Optimize: Ensure the stability and performance of our ML models and AI APIs — testing should be a given.
- Explore the new: Evaluate cutting-edge technologies in Machine Learning Ops, Feature Stores, Storage, Computing, and Orchestration — if it's open source and promising, you'll work with it.
Requirements
- Deep knowledge of software engineering, including testing and design patterns.
- Experience with data pipelines, ML data preprocessing, feature engineering, and storage techniques.
- A "get-code-into-prod" mentality — you value robustness and performance.
- You thrive in agile, cross-functional teams that deliver fast and learn faster.
Your Tools
- Python: Building clean, scalable data pipelines and ML backends, including frameworks like TensorFlow, PyTorch, or scikit-learn.
- SQL: Mastering efficient queries, complex joins, and large datasets.
- Git: Version control is in your blood. You know how to branch, commit, and collaborate cleanly.
Bonus Skills (nice to have):
- Apache Hadoop, Spark
- Docker, Kubernetes
- Grafana, Prometheus, Graylog
- Jenkins
- Java, Scala
- Shell Scripting
Our Tech Stack
We build with the tools we love (and we love good tools): TypeScript, Node.js, React, Python, SQL, Scala, Java, Docker, Kubernetes, AWS, GCP, C++, GitHub, and the occasional caffeine-fueled whiteboard sketch. And we are not afraid to discard something if there is a better way.
Benefits
- High-quality hardware (Mac/Linux/whatever you need).
- Access to Coursera, Udacity, conferences, hackathons & coaching.
- "Smart Fridays" – our 4-day week to protect your flow.
- Flexible working hours & remote work – we trust you to get your job done.
- JobRad + Urban Sports Club deals + free RTL+ subscription.
- Deutschlandticket subsidy, great team events, and more.
Requirements
- Tiefes Wissen in Software Engineering, inkl. Testing und Design Patterns
- Erfahrung mit Datenpipelines, ML-Datenvorverarbeitung, Feature Engineering und Storage-Techniken
- Eine „get-code-into-prod“-Mentalität — du legst Wert auf Robustheit und Performance
- Du blühst in agilen, cross-funktionalen Teams auf, in denen schnell geliefert und schneller gelernt wird
Responsibilities
- Design, Build & Run: Architektur skalierbarer Data-Backends für unsere KI- und ML-getriebenen Produkte.
- Brücke schlagen: Eng mit Data Scientists zusammenarbeiten, um ML-Prototypen in Produktion zu bringen
- Automatisieren & Optimieren: Stabilität und Performance unserer ML-Modelle und KI-APIs sichern — Tests sollen selbstverständlich sein.
- Neues erkunden: Cutting-Edge-Technologien in Machine Learning Ops, Feature Stores, Storage, Computing und Orchestrierung evaluieren — wenn es Open Source und vielversprechend ist, wirst du damit arbeiten.
Benefits
Skills
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