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MLOps Engineer
Summary by AIEngineer.careers
MLOps Engineer is a MLOps Engineer role (full-time). with MLOps. in CITY OF AMSTERDAM, US. Imported listing (source: altrix.likesyou.org). Apply on the employer's site (altrix.likesyou.org).
Imported listing (source: altrix.likesyou.org) · Apply on altrix.likesyou.org
CITY OF AMSTERDAM · US
—
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from altrix.likesyou.org
Adyen is looking for an MLOps Engineer to join its central MLOps team, building platforms and tools that serve all data science teams. In this role, you'll own the deployment, operation, and scaling of machine learning models in both real-time and batch flows, covering everything from experiment tracking to monitoring. The role is based in Adyen's Amsterdam office, working closely with product ML teams to identify pain points and bring MLOps maturity to the next level.
Responsibilities
- Own, develop, deploy, and operate MLOps tooling and services
- Ensure safe, stable, and performant ML model deployments in real-time and batch flows
- Manage experiment tracking, validation, and hyperparameter optimization runs
- Monitor models for downtime, latency, and drift
- Scale MLOps infrastructure and increase MLOps maturity
- Build tools to democratize machine learning practices at Adyen
- Collaborate with product machine learning teams to improve workflows
Requirements
- 4+ years of professional experience as a DevOps Engineer, MLOps Engineer, ML Engineer, or Data Engineer
- Strong software development skills, including version control (Git/GitLab), debugging, and unit/integration testing
M
MLOps
Denver · US
MLOps (Machine Learning Operations) is a set of practices, principles, and cultural shifts designed to unify machine learning application development with system deployment and operations. It provides a framework for automating the end-to-end lifecycle of machine learning models, including data preparation, training, testing, deployment, monitoring, and retraining.
