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
What we're looking for

• 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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