Job Description

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.