MLOps Engineer: Cloud AI Pipelines & Monitoring

Springer Nature; Groningen; Hybrid; EUR 70,000 - 95,000; Full time

Job summary

Springer Nature AI Lab (SNAIL) seeks an experienced MLOps Engineer in Groningen to design, deploy, and scale AI solutions across cloud environments. You will bridge ML development and production operations, ensuring reliability, security, and governance while enabling fast, responsible AI delivery.

The role emphasizes collaboration, tooling with Python, FastAPI, Docker, and Langfuse, and mentoring teammates within a fast-paced environment.

Qualifications

• Requires degree in Software Engineering, Computer Science, AI, or related field.
• Strong Python development experience.
• Experience with GitHub, Docker, PyTorch or TensorFlow.
• Hands-on with Azure/AWS/GCP cloud platforms.
• Experience building APIs with FastAPI or similar.
• Knowledge of CI/CD, testing, and GitHub Actions.
• Experience in observability, monitoring, and tracing for AI/ML systems.

Responsibilities

• Design, build, and maintain scalable MLOps platforms and deployment pipelines.
• Develop cloud-native services, automation workflows, and CI/CD to improve deployment velocity.
• Implement monitoring, tracing, and performance management for production systems.
• Drive model lifecycle governance, reliability, and operational resilience.
• Contribute to data security, governance, and responsible AI practices.
• Document and share knowledge to increase transparency and adoption of AI capabilities.
• Mentor junior engineers and stay updated on AI engineering trends.

Skills

Python

GitHub

PyTorch

TensorFlow

Cloud platforms (Azure/AWS/GCP)

APIs (FastAPI)

CI/CD (GitHub Actions)

Observability/Monitoring

Langfuse

Education

Bachelor's degree in Software Engineering or Computer Science

Tools

Docker

FastAPI

Langfuse

GitHub Actions

Job description

Springer Nature AI Lab (SNAIL) seeks an experienced MLOps Engineer in Groningen to design, deploy, and scale AI solutions across cloud environments. You will bridge ML development and production operations, ensuring reliability, security, and governance while enabling fast, responsible AI delivery.

The role emphasizes collaboration, tooling with Python, FastAPI, Docker, and Langfuse, and mentoring teammates within a fast-paced environment.