ML Deployment Engineer

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