ML/Data Infrastructure Engineer

Try&Hire; Paudex; Hybride; CHF 120 000 - 170 000; Plein temps

Avantages offerts par ce poste

Hybrid work model

Remote up to 2 days per week

Interim 3–6 months then permanent

Résumé du poste

Try&Hire is seeking an experienced Data/ML Infrastructure Engineer to own the ML data pipelines and deployment tooling. You will oversee data pools, annotation workflows with partners, and automation for training, evaluation and deployment.

The role emphasizes production-grade Python, AWS S3, Docker, and ML lifecycle tooling, with hybrid work options and potential for a permanent position after a 3–6 month interim period. Proficiency in English; French is a plus.

Qualifications

• 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.

Responsabilités

• 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.

Connaissances

Data engineering

MLOps

Large-scale data processing

English proficiency

French language

Outils

Python

AWS

S3

Docker

MLflow

DVC

SageMaker

Description du poste

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