Senior ML Engineer | Germany (3 Month project)

Intetics; Deutschland; Vor Ort; EUR 80.000 - 110.000; Vollzeit

Zusammenfassung

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

You will develop Python-based pipelines, orchestrate Kubeflow, run experiments with MLflow, and ensure secure, scalable deployments in a zero-trust environment.

Qualifikationen

• Hands-on experience with Kubeflow Pipelines (KFP v2).
• Experience training models on GPUs in production.
• Practical experience with LLMs and transformer fine-tuning.
• Experience with MLflow and MLOps in cloud-native environments.
• Knowledge of large-scale data processing with SQL.

Aufgaben

• 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.

Kenntnisse

Kubeflow Pipelines

GPUs

LLM fine-tuning

MLflow

XGBoost/CatBoost

Python

SQL

CI/CD

Testing

MLOps

Kubernetes

Tools

DuckDB

SQL Server

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

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.