Senior ML Engineer - Germany (3 Month project)

Intetics 2; Deutschland; Vor Ort; EUR 70.000 - 110.000; Vollzeit

Zusammenfassung

Intetics 2 seeks an experienced ML Engineer / MLOps Engineer to join a cloud-native project for a German customer. The role centers on building production-grade ML infrastructure, GPU workloads, ML pipelines, LLMs and large-scale data processing.

You'll work with Kubeflow, Kubernetes, Python tooling and CI/CD, ensuring high engineering standards in a secure enterprise environment. German B2+ and English B1+ are required, with a start date around September 30, 2026.

Qualifikationen

• Hands-on experience with Kubeflow Pipelines (KFP v2).
• Experience training models on GPUs.
• Fine-tune LLMs/transformers in production.
• Experience with MLflow for experiment tracking.
• Strong Python engineering skills.
• Solid SQL experience and large-scale data processing.
• Production-grade ML/MLOps in secure cloud-native environments.
• Experience with CI/CD in enterprise environments.

Aufgaben

• Build and orchestrate ML pipelines using Kubeflow Pipelines (KFP v2).
• Train models on GPUs and manage GPU resources in Kubernetes.
• Fine-tune transformers and LLMs for production use.
• Track experiments and models with MLflow.
• Develop Python-based pipelines, integrations and tooling.
• Maintain CI/CD pipelines and testing standards with GitLab CI.
• Ensure secure, zero-trust environment with network policies.

Kenntnisse

Kubeflow Pipelines

GPUs

Python

LLMs/Transformers

MLflow

Kubernetes

CI/CD

Secure environments

Tools

Kubeflow Pipelines

MLflow

XGBoost

CatBoost

SQL Server

DuckDB

Kubernetes

GitLab CI

Jobbeschreibung

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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