DataOps / MLOps Engineer - European Tech Recruit
Pubblicato il 15-08-2026 - European Tech Recruit in Bressanone
European Tech Recruit are working closely with a market leading 3D scanning company, based in Bressanone, who are looking for a talented DataOps / MLOps Engineer to join their team.
In this role you will join a company that leverages state-of-the-art Computer Vision and Machine Learning algorithms to scan high quality, relightable 3D models of objects and products at scale.
You will help to build the infrastructure that powers their data and ML workflows. You'll focus on data storage and movement, dataset versioning, ML pipeline automation, experiment tracking, and ensuring reproducibility across the 3D reconstruction and training workloads.
Responsibilities as DataOps / MLOps Engineer
- Design and manage data storage systems for large datasets (multi-TB image data, 3D assets, training data).
- Build efficient data access patterns and movement strategies for distributed training and experimentation.
- Implement dataset versioning and lineage tracking for reproducibility
- Set up and maintain experiment tracking and model registry infrastructure (MLflow, Weights & Biases).
- Build ML pipelines for data preprocessing, training, validation, and model registration (Kubeflow, Airflow, Prefect).
- Support distributed training workflows across multi-GPU clusters (PyTorch Distributed, Horovod, Ray).
- Profile and optimize training pipelines: data loading bottlenecks, batch sizing, GPU memory utilization.
- Ensure reproducibility of experiments: environment pinning, data versioning, artifact management.
- Manage artifact storage and distribution (Docker registries, model registries, package repositories).
- Build tooling to improve developer productivity for ML workflows.
Requirements
- Experience with data storage systems and large file handling (object storage, NFS, distributed filesystems).
- Knowledge of dataset versioning tools (DVC, Delta Lake, or similar).
- Experience with ML pipeline orchestration (Airflow, Prefect, Kubeflow).
- Familiarity with experiment track