Remote MLOps Engineer: Build & Scale Production ML Pipelines

Redcare Pharmacy; United States; Remote; USD 77,000 - 111,000; Full time

Job summary

Redcare Pharmacy is seeking a Mid-Level MLOps Engineer to join the Data & AI function. This permanent, full-time role is listed as fully remote within Germany in a distributed engineering setup, with Köln as the hiring location and a 40-hour workweek.

The role focuses on productionizing ML models and maintaining scalable ML pipelines within a regulated pharmaceutical platform. You will automate training, deployment, monitoring, and lifecycle management, collaborating with data scientists, ML

Qualifications

• Mid-level MLOps experience or similar platform/ML engineering role.
• Hands‑on experience with tooling to build, deploy and monitor ML models in production.
• Comfort with cloud infrastructure, containerization, and orchestration relevant to ML workloads.

Responsibilities

• Build and maintain infrastructure, pipelines, and tooling to productionize ML models.
• Automate training, deployment, monitoring, and lifecycle management of ML systems.
• Collaborate with data scientists, ML engineers, and software engineers to move models to production.
• Operate and improve cloud-based platforms supporting scalable ML workloads.
• Contribute to reliability, observability, and incident response for ML services and data pipelines.
• Support engineering practices within the Tech department as a direct-labor engineering hire.

Skills

MLOps experience

ML deployment tooling

Cloud infrastructure

Containers

Orchestration concepts

Remote team work

Job description

Redcare Pharmacy is seeking a Mid-Level MLOps Engineer to join the Data & AI function. This permanent, full-time role is listed as fully remote within Germany in a distributed engineering setup, with Köln as the hiring location and a 40-hour workweek.

The role focuses on productionizing ML models and maintaining scalable ML pipelines within a regulated pharmaceutical platform. You will automate training, deployment, monitoring, and lifecycle management, collaborating with data scientists, ML