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

Ihre Aufgaben

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

Was Sie mitbringen

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

Rahmenbedingungen

Pensum: 100 %
Arbeitsmodell: Hybrid
Option auf Festanstellung nach dem Einsatz, wenn beide Seiten wollen

Was Sie erwartet

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

Technologien

Python
AWS
S3
Docker
MLflow
DVC
SageMaker

So bewerben Sie sich

Sie bewerben sich mit Ihrem Try&Hire-Profil: Lebenslauf hochladen, Angaben prüfen, Eckdaten und Verfügbarkeit eintragen. Ein kurzes Vorstellungsvideo ist freiwillig, hilft aber. Unser Team meldet sich, wenn Ihr Profil zum Einsatz passt.

ML/Data Infrastructure Engineer