MLOps Engineer (m/f/d)

Job summary

The MLOps Engineer role is centered on implementing and managing CI/CD pipelines and automated testing for AI/ML workloads. Responsibilities include building model registries, maintaining observability with tools like Prometheus and Grafana, and executing infrastructure-as-code using Docker and Kubernetes in cloud environments.

Qualifications

• 3-5 years of experience in DevOps, cloud engineering, ML engineering, or MLOps.
• Hands-on experience with CI/CD and automated deployment in production.
• Strong Python skills and Git-based development workflows.
• Experience with Docker, Kubernetes, and Helm.
• Working knowledge of Azure or AWS cloud services.
• Experience with observability tools like Prometheus and Grafana.
• Familiarity with MLOps concepts such as model registries and lifecycle management.
• Understanding of network isolation and API access control.
• Fluency in English, spoken and written.

Responsibilities

• Implement and operate CI/CD pipelines and automated testing for AI/ML workloads.
• Build and maintain model registry and AI gateway integrations for LLM APIs.
• Configure observability for model usage and quality signals using Prometheus and Grafana.
• Support transition of workloads from sandbox to production environments.
• Implement reusable components for LLM API integration and evaluation pipelines.
• Execute infrastructure-as-code for container and cloud environments.
• Maintain technical documentation and operational dashboards.
• Support incident analysis and reliability improvements for production AI workloads.
• Collaborate with integration and security teams on deployment requirements.

Skills

Languages

Relocation