Description review
Senior ML Engineer | Germany (3 Month project)
Intetics 2 · Germany · back to the listing
HR standards
45/100
poor
Title ↔ description
82/100
solid
Reads as
Machine Learning Engineer
100% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
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Startups
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What the listing never says
- 24 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No section describes what the person would actually do. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
- No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
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.
Find more English Speaking Jobs in Germany on Arbeitnow
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.
Find more English Speaking Jobs in Germany on Arbeitnow