Senior MLOps Engineer (m/f/d)

Rohde & Schwarz GmbH & Co.; Dresden; Hybrid; EUR 70.000 - 110.000; Vollzeit

Zusammenfassung

Rohde & Schwarz GmbH & Co. in Dresden, Germany seeks an AI/ML Engineer to take models from prototype to production.

You will build end-to-end ML pipelines for ingestion, training and deployment, and implement CI/CD for models in a hybrid cloud/on-prem environment. You will containerize AI applications, deploy on Kubernetes, Azure and edge targets, and optimize latency, accuracy, and energy efficiency while monitoring drift and conducting A/B tests.

Qualifikationen

• Experience building ML pipelines for data ingestion, training and deployment (≤140 chars)
• CI/CD processes for ML models experience (≤140 chars)
• Deployment on Kubernetes, Azure and edge targets (≤140 chars)

Aufgaben

• Implement and maintain ML pipelines end-to-end for production models.
• Containerize and package AI applications for hybrid cloud and on-prem environments.
• Deploy models on Kubernetes, Azure and edge targets, optimizing latency and energy use.
• Implement monitoring, logging, drift detection and A/B testing for production models.
• Ensure reproducibility with experiment tracking, versioning and metadata management.
• Collaborate with AI/ML engineers to productionize prototypes.

Kenntnisse

ML pipelines

CI/CD

Kubernetes

Azure

Edge deployment

Model monitoring

Experiment tracking

Collaboration with ML engineers

Tools

Docker

Kubernetes

Azure

Jobbeschreibung

Introduction

You take AI models from prototype to production—scalable, robust, and ready for the real world.

Tasks

• You implement and maintain ML pipelines for data ingestion, training and deployment as well as CI/CD processes for models.
• You containerize and package AI applications for hybrid cloud and on-premise environments.
• You deploy models on Kubernetes, Azure and edge target systems and optimize them in terms of latency, accuracy and energy efficiency.
• You implement monitoring, logging, drift detection and A/B testing for production models.
• You ensure reproducibility and experiment tracking throughout the entire model lifecycle, including versioning and metadata management.
• You work closely with AI/ML engineers to make prototypes production-ready.