ML Deployment Engineer

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.

• 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 product ionise new models without requiring them to manage the underlying infrastructure

• 3+ years in MLOps, ML Engineering, ML Infrastructure, Platform Engineering, or similar roles
• KServe or comparable model-serving technology
• AWS and/or GCP
• Strong understanding of production ML systems

• NVIDIA Triton Inference Server
• Ray Serve
• PyTorch / Tensor Flow
• Terraform
• Canary or blue-green deployments
• GPU-enabled inference workloads
• Model monitoring and drift detection
• Experience operating real-time inference APIs