ML Data Infra Engineer - Remote Options & 25 Vacation Days

Flyability; Paudex; Hybrid; CHF 120.000 - 170.000; Vollzeit

Benefits dieser Stelle

25 vacation days

Remote up to 2 days/week

Accident insurance

Flexible schedules

Gym discounts

Swibeco discounts

Team events

Zusammenfassung

Flyability in Lausanne, Switzerland, seeks a ML/Data Infrastructure Engineer to build and operate the data and ML infrastructure that turns drone data into smarter AI capabilities. You will own the end-to-end ML data lifecycle—from data ingestion and labeling to training, evaluation and deployment—collaborating with Spatial AI and Cloud Platform teams.

You should have 3+ years in data engineering or MLOps, strong Python, AWS experience, and a drive to create scalable, reproducible ML systems.

Qualifikationen

• 3+ years in data engineering, MLOps or ML infrastructure.
• Strong Python and software engineering skills; production-grade systems.
• Experience with data storage, databases, and large datasets.
• AWS experience, especially S3 and cloud compute/storage.
• Familiarity with ML lifecycle: versioning, experiment tracking, training orchestration.
• Knowledge of pipelines, labeling workflows, and data quality.

Aufgaben

• Own ML data pools and datasets from ingest to versioning and validation.
• Host in-house labeling tools and manage annotation workflows.
• Automate training, evaluation, experiment tracking, and reproducibility.
• Build tooling to move models from training/validation to deployment.
• Monitor training and model performance; integrate feedback loops.
• Provide tools and docs for Spatial AI engineers to deploy models.

Kenntnisse

Python

MLOps

Data engineering

CI/CD

Docker

AWS

Dataset versioning

Experiment tracking

Databases

English

Tools

Docker

CI/CD

Workflow orchestration

IaC

Jobbeschreibung

Flyability in Lausanne, Switzerland, seeks a ML/Data Infrastructure Engineer to build and operate the data and ML infrastructure that turns drone data into smarter AI capabilities. You will own the end-to-end ML data lifecycle—from data ingestion and labeling to training, evaluation and deployment—collaborating with Spatial AI and Cloud Platform teams.

You should have 3+ years in data engineering or MLOps, strong Python, AWS experience, and a drive to create scalable, reproducible ML systems.