DataOps & MLOps Engineer

About ALLSIDES

ALLSIDES is redefining how the world experiences 3D content. We combine physically accurate scanning and generative AI to power content creation workflows for e-commerce, virtual environments, and immersive experiences. Our clients include global brands like adidas, Meta, Amazon, and Zalando.

We operate a rapidly scaling photorealistic 3D scanning operation, capturing tens of thousands of assets annually while training next-generation AI models. As an NVIDIA Inception member, we collaborate with leading research institutions and actively participate in top-tier conferences in 3D computer vision and AI.

More info: https://www.allsides.tech | https://blogs.nvidia.com/blog/covision-adidas-rtx-ai/

Position Overview

We're looking for a DataOps & MLOps Engineer to build the infrastructure that powers our data and ML workflows. You'll focus on data storage and movement, dataset versioning, ML pipeline automation, experiment tracking, and ensuring reproducibility across our 3D reconstruction and training workloads.

Main Responsibilities

- 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

Qualifications

- Strong Linux knowledge
- 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 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

Nice to Have

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

What we offer

- Compensation that reflects your experience including stock-options
- Lunch voucher for working days
- We assist with relocation
- Flexible working hours and work-from-home policy
- Family-friendly environment
- Amazing office space in South Tyrol, located at the Durst Group
- Personal and professional growth opportunities

You don't have to tick every box to apply, your drive and passion matter most

This role is located on-site in Brixen/Bressanone, Italy. If you are interested, please apply with your CV attached to careers@allsides.tech