You will build and operate the data and ML infrastructure that turns drone-collected data into deployable machine-learning capabilities.

Your tasks

Own infrastructure and workflows for ML data pools and datasets, from ingestion to versioning
Host in-house labeling tools and manage annotation workflows with external partners
Automate and maintain infrastructure for ML training, evaluation and experiment tracking
Build tooling and automation to move models from training to reliable deployment
Monitor training and model performance and feed production data back into development
Provide tools, documentation and workflows enabling engineers to go from data to deployable models

What you bring

3+ years of experience in data engineering, MLOps, ML infrastructure or related software engineering
Strong Python and software engineering skills with production-grade systems experience
Hands-on experience with data storage, databases and large-scale data processing
Practical experience with AWS, particularly S3 and cloud-based compute and storage
Experience with ML lifecycle: versioning, experiment tracking, training orchestration, model registries or ML CI/CD
Experience with Docker, CI/CD, workflow orchestration or infrastructure-as-code
Proficiency in English; French is a plus

Conditions

Workload: 100 %
Work model: Hybrid
Option of a permanent position after the assignment if both sides want it

What to expect

Flexible schedules with option to work remotely up to 2 days per week
3–6 months interim, then a permanent position

Technologies

Python
AWS
S3
Docker
MLflow
DVC
SageMaker

How to apply

You apply with your Try&Hire profile: upload your CV, check the details, enter key facts and availability. A short introduction video is optional but helps. Our team will get in touch if your profile fits the assignment.

ML/Data Infrastructure Engineer