ML Deployment Engineer
Munich, Germany — Hybrid
Deep Tech | MLOps | Model Deployment | Model Serving | ML Infrastructure
Our client, a growing Deep Tech / AI company based in Munich, is looking for an ML Deployment Engineer to build the deployment layer that takes machine-learning models from experimentation into scalable, reliable production services.
You'll work at the intersection of ML Engineering, MLOps, and Platform Engineering, creating the tooling and infrastructure that makes model deployment repeatable, observable, and production-ready.
What You'll Work On
• Build production deployment pipelines for machine-learning models
• Deploy and operate model-serving workloads on Kubernetes
• Build scalable inference services using KServe
• Containerise ML workloads using Docker
• Develop deployment tooling and automation in Python
• Manage model versions, artefacts, and deployment workflows with MLflow
• Build CI/CD pipelines for testing and releasing ML services
• Deploy workloads across AWS and/or GCP environments
• Implement rollout, rollback, and model versioning strategies
• Improve deployment reliability, scalability, and observability
• Automate the path from approved model to production endpoint
• Collaborate with ML Engineers to productionise new models without requiring them to manage the underlying infrastructure
Core Skills
• 3+ years in MLOps, ML Engineering, ML Infrastructure, Platform Engineering, or similar roles
• Python
• Kubernetes
• Docker
• KServe or comparable model-serving technology
• MLflow
• AWS and/or GCP
• CI/CD
• Strong understanding of production ML systems
Nice to Have
Argo CD / GitOps
Kubeflow
NVIDIA Triton Inference Server
Ray Serve
PyTorch / TensorFlow
Prometheus / OpenTelemetry
Terraform
Canary or blue-green deployments
GPU-enabled inference workloads
Model monitoring and drift detection
Experience operating real-time inference APIs