Grape Up
Távmunka
1.5M – 2.1M HUF vállalkozói havonta+ÁFA / 1.2M – 1.8M HUF alkalmazotti havi bruttó
Elvárások
- Python
- SQL
- MLflow
- PyTorch
- TensorFlow
- Databricks
- AWS SageMaker
- Angol (C1)
- Lengyel (C1)
Előnyt jelentő készségek
- Docker
- Kubernetes
- Terraform
- CloudFormation
- PhD in CS, DE or AI
A mi követelményeink
Eredeti szöveg. Fordítás megjelenítése
- Master’s degree in computer science, Machine Learning, Data Engineering, or a related field
- 3+ years of professional experience in ML Engineering, MLOps, or DevOps, with hands-on exposure to production ML systems
- Strong Python programming skills and proficiency with ML frameworks (PyTorch, TensorFlow, scikit-learn)
- Experience with key parts of the ML lifecycle: experiment tracking (e.g. MLFlow), workflow orchestration, model deployment, and production operations
- Hands-on experience with Databricks or AWS SageMaker, or strong willingness to deepen expertise in one of these platforms
- Experience deploying and operating ML systems preferably on cloud platforms (Azure or AWS)
- Experience with model monitoring, observability, and performance tracking
- Strong problem-solving skills and ability to work independently in fast-paced environments
- Fluency in English and Polish both written and spoken
Pozíció / projekt rövid leírása
Eredeti szöveg. Fordítás megjelenítése
At Grape Up, we transform businesses by unlocking the potential of AI and data through innovative software solutions.
We partner with industry leaders in the automotive and aviation to build sophisticated Data & Analytics platforms that support production machine learning and AI use cases. Our solutions provide comprehensive capabilities spanning data storage, management, advanced analytics, machine learning, enabling enterprises to accelerate innovation and make trusted, data-driven decisions.
Napi feladatok
Eredeti szöveg. Fordítás megjelenítése
- Partner with Data Science teams to productionize models and work across the ML lifecycle – from experimentation and training to deployment, monitoring, and continuous improvement
- Design and implement scalable ML infrastructure, with the opportunity to take ownership of architecture and deployment decisions
- Build and maintain CI/CD pipelines for model development, testing, and deployment on Databricks or AWS SageMaker
- Establish MLOps best practices: experiment tracking, model versioning, feature stores, and governance (MLflow, Unity Catalog, or SageMaker ecosystem)
- Monitor and optimize ML infrastructure for performance, cost efficiency, and reliability
- Work on real-world ML systems running in production – not just experimental models
Specifikációk
- Azonnali kezdés
- Távmunka
Biztosított eszközök
- Apple
- Windows
- Számítógép: Notebook
- Monitorok: kettő
Irodán belüli juttatások
- Ingyenes kávé
- Belső képzések
- Modern iroda
- Startup hangulat
- Nincs dress code
- Zuhanyzó
- Ingyenes italok
- Ingyenes parkolás
- Szórakozási zóna
- Kerékpártároló
Extrák
- Képzési költségvetés
- Magánegészségügyi ellátás
- Lapos szervezet
- Kis létszámú csapat
- Nemzetközi projektek
