Deutsche Telekom IT Solutions Hungary
Budapest, Debrecen, Pécs, Szeged
20% irodai munkavégzés, 80% távmunka
1.2M – 2.3M HUF alkalmazotti havi bruttó
Elvárások
- MLOps
- Production ML
- Phyton
- PyTorch/TensorFlow
- MLflow
- Kubeflow
- Terraform
- Cloud
- Angol (Anyanyelvi)
Előnyt jelentő készségek
- Német
A mi követelményeink
Eredeti szöveg. Fordítás megjelenítése
Requirements:
- Experience: Senior-level experience in MLOps or Production ML.
- Tech Stack: Strong Python, ML Frameworks (PyTorch/TensorFlow), and MLOps tools (MLflow, Kubeflow, or SageMaker).
- IaC: Experience with Terraform and Cloud optimization.
- Defense Readiness: EU Citizenship is mandatory.
- Language: Fluent English; German is a strong plus.
Pozíció / projekt rövid leírása
Eredeti szöveg. Fordítás megjelenítése
Company Description
As Hungary’s most attractive employer in 2025 (according to Randstad’s representative survey), Deutsche Telekom IT Solutions is a subsidiary of the Deutsche Telekom Group. The company provides a wide portfolio of IT and telecommunications services with more than 5300 employees. We have hundreds of large customers, corporations in Germany and in other European countries. DT-ITS recieved the Best in Educational Cooperation award from HIPA in 2019, acknowledged as the the Most Ethical Multinational Company in 2019. The company continuously develops its four sites in Budapest, Debrecen, Pécs and Szeged and is looking for skilled IT professionals to join its team.
Job Description
You will bridge the gap between Data Science and Operations. Your goal is to operationalize complex ML models for Defense applications, ensuring that AI-driven insights are delivered to the field with „Zero-Downtime” reliability and full traceability.
* Please be informed that our remote working possibility is only available within Hungary due to European taxation regulation.
Napi feladatok
Eredeti szöveg. Fordítás megjelenítése
- End-to-End Lifecycle: Build and manage the full ML lifecycle—from experiment tracking to model deployment and retraining.
- Containerization: Master the deployment of ML workloads using Docker and Kubernetes (OpenShift).
- Automation: Implement ML-specific CI/CD (e.g., CML, Kubeflow Pipelines) to automate the promotion of models to production.
- Observability: Set up specialized monitoring for model drift, data quality, and prediction accuracy.
- Scalability: Architect distributed systems for large-scale model inference.
Specifikációk
- Azonnali kezdés
- Távmunka heti 4 nap
Biztosított eszközök
- Számítógép: Notebook
- Monitorok: kettő
Irodán belüli juttatások
- Ingyenes kávé
- Menza
- Kerékpártároló
- Szórakozási zóna
- Zuhanyzó
- Ingyenes parkolás
- Belső képzések
- Belső hack napok
- Modern iroda
- Nincs dress code
Extrák
- Sportcsomag
- Magánegészségügyi ellátás
- Nemzetközi projektek
- Képzési költségvetés
- Mobiltelefon
