DataOps​/MLOps Engineer

Position: DataOps / MLOps Engineer
Location: San Michele All'Adige

Data Ops / MLOps Engineer

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European Tech Recruit are working closely with a market leading 3D scanning company, based in Bressanone, who are looking for a talented  Data Ops / MLOps Engineer  to join their team.

In this role you will join a company that leverage 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  Data Ops / 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:
Strong Linux knowledge.

Experience with data storage systems and large file handling (object storage, NFS, distributed file systems).
Knowledge of dataset versioning tools (DVC, Delta Lake, or similar).

Experience with ML pipeline orchestration (Airflow, Prefect, Kubeflow).
Familiarity with experiment tracking tools (MLflow, Weights & Biases, Neptune).
Understanding of distributed training frameworks and patterns.

Experience with containerization (Docker) and CI/CD pipelines.
Knowledge of Python dependency and environment management.

Desirable

Experience:

Experience with model registries and deployment workflows.
Familiarity with data quality validation frameworks.
Knowledge of 3D graphics processing or computer vision workflows. xpavfwm

If this role is of any interest please apply directly on Linked In or send a copy of your CV to .

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