Senior ML Engineer | Germany (3 Month project)
Sign up free to see how well your resume matches this role.
What you'll do
- 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
What they'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
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
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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.
Company
Company facts come from this company's own listings. We only show what the postings themselves carry.