We are looking for an experienced We are looking for an experienced ML Engineer / MLOps Engineer to join a cloud-native project for a German customer. The role is strongly engineering-focused and involves building production-grade ML infrastructure, working with GPU workloads, ML pipelines, LLMs and large-scale data processing.

Location: Germany

German: B2+ - must-have

English: B1+

Estimated start: September 30, 2026

What you'll be working on

Build and orchestrate ML pipelines using Kubeflow Pipelines (KFP v2)
Train ML models on GPUs and manage GPU resources within Kubernetes
Fine-tune transformers and LLMs
Track experiments and models using MLflow
Build classical ML models with XGBoost and CatBoost
Process large datasets using SQL Server and DuckDB
Develop Python-based pipelines, integrations and tooling
Maintain high engineering standards through testing, clean code and CI/CD with GitLab CI
Work in a secure, zero-trust / secure-by-default environment with network policies and restrictive container permissions

Requirements

Hands-on experience with Kubeflow Pipelines, ideally KFP v2
Experience training models on GPUs
Practical experience with LLM / transformer fine-tuning
Experience with MLflow
Strong knowledge of XGBoost, CatBoost or similar boosting models
Strong Python engineering skills
Solid SQL experience and understanding of large-scale data processing
Experience with CI/CD, clean code and automated testing
Production-grade ML/MLOps experience beyond notebook-based experimentation
Experience working in enterprise or regulated cloud-native environments
Nice to have Experience with LLM pre-training, beyond fine-tuning GPU orchestration in Kubernetes
Experience with zero-trust environments, network policies and restrictive container rights
Knowledge of DuckDB
Experience with modern Python tooling such as uv
Previous healthcare or billing domain experience is not required, but you should be comfortable quickly getting up to speed with a new domain.

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