Senior MLOps Engineer — Hybrid, Automate ML Production

Vinted; Amsterdam; Hybride; EUR 89 000 - 121 000; Plein temps

Avantages offerts par ce poste

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25 days annual leave

Newest MacBook models

Employee Assistance Program (EAP)

Team-building events

Home office budget: up to €540

Daily lunch allowance

Company bike leasing

Flexible benefits platform budget

Work from Home allowance

Pension plan

NS mobility card

Dog-friendly office

Résumé du poste

Vinted seeks an MLOps Engineer to bridge Data Science and Infrastructure, standardize production workflows, and automate model training, evaluation, and deployment with GCP Vertex AI, Vespa, and Triton. You’ll collaborate across teams, improve quality gates, and shape end-to-end ML lifecycle governance for scalable models.

You’ll join an active engineering community with learning workshops, home-office support, and a strong emphasis on pragmatism and high-quality production readiness.

Qualifications

• ML workflows and concepts understood well enough to debug pipelines.
• Experience building robust CI/CD and automation pipelines.
• Ability to bridge Data Science and Infrastructure teams.

Responsabilités

• Productionise ML models by migrating training code into automated pipelines.
• Design and maintain continuous training pipelines for data prep and model scoring.
• Package and deploy models to Vespa or Triton in production.
• Automate deployment processes, e.g., GitHub Actions to Vespa via webhooks.
• Build automated quality gates and lifecycle standards for ML models.

Connaissances

GCP/Vertex AI

Kubernetes

Docker

CI/CD automation

GitHub Actions

Jenkins

ML workflows

Cross-functional collaboration

Service mindset

Outils

Vespa

NVIDIA Triton

ONNX

Vertex AI

GCP

Description du poste

Vinted seeks an MLOps Engineer to bridge Data Science and Infrastructure, standardize production workflows, and automate model training, evaluation, and deployment with GCP Vertex AI, Vespa, and Triton. You’ll collaborate across teams, improve quality gates, and shape end-to-end ML lifecycle governance for scalable models.

You’ll join an active engineering community with learning workshops, home-office support, and a strong emphasis on pragmatism and high-quality production readiness.