Dataops / Mlops Engineer

European Tech Recruit; Lazio; In loco; EUR 40.000 - 70.000; Tempo pieno

Descrizione del lavoro

A leading technology recruitment company is seeking a skilled DataOps / MLOps Engineer in Italy, Lazio. In this role, you will leverage advanced Machine Learning strategies and Computer Vision techniques to develop data infrastructures for 3D modeling. Responsibilities include managing large datasets, creating efficient data access patterns, and automating ML pipelines. Candidates should have experience with containerization, data storage systems, and dataset versioning tools. Interested candidates can apply via LinkedIn or send CV to [e-mail].

Competenze

• Experience with data storage systems and large file handling.
• Knowledge of dataset versioning tools like DVC or Delta Lake.
• Experience with ML pipeline orchestration tools.
• Familiarity with experiment tracking tools such as MLflow.
• Understanding of distributed training frameworks.

Mansioni

• Design and manage data storage systems for large datasets.
• Build efficient data access patterns for distributed training.
• Implement dataset versioning and tracking for reproducibility.
• Set up experiment tracking and model registry infrastructure.
• Build ML pipelines for data preprocessing and model registration.

Conoscenze

Data storage systems expertise

Dataset versioning tools knowledge

ML pipeline orchestration experience

Experiment tracking tools familiarity

Distributed training frameworks understanding

Containerization experience

Python dependency management

Model registry experience

Data quality validation frameworks knowledge

3D graphics processing knowledge

Strumenti

DVC

Delta Lake

Airflow

Kubeflow

Docker

Weights & Biases

Neptune

PyTorch

Horovod

Ray

Descrizione del lavoro

Overview

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 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

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

How to apply

If this role is of any interest please apply directly on LinkedIn or send a copy of your CV to [e-mail].

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