Senior ML Engineer – 3 Month Project

Jobtailor; Deutschland; Remote; EUR 80.000 - 120.000; Vollzeit

Zusammenfassung

Jobtailor seeks an experienced ML Engineer to build and orchestrate ML pipelines using Kubeflow, train models on GPUs, and fine-tune transformers and LLMs. You will implement Python-based pipelines, tools and integrations to support data processing at scale.

You will work in a regulated cloud-native environment, maintain CI/CD standards with GitLab CI and MLflow for experiment tracking, and collaborate with cross-functional teams in a German-speaking enterprise.

Qualifikationen

• Hands-on experience with Kubeflow Pipelines (KFP v2).
• Experience training models on GPUs.
• Practical experience with LLM / transformer fine-tuning.
• Experience with MLflow.
• Strong Python engineering skills.
• Solid SQL experience and 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.
• German proficiency at B2+ level; English at B1+ level.

Aufgaben

• Build and orchestrate ML pipelines using Kubeflow Pipelines.
• Train ML models on GPUs and manage GPU resources within Kubernetes.
• Fine-tune transformers and LLMs.
• Track experiments and models using MLflow.
• Develop Python-based pipelines, integrations and tooling.
• Maintain 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

GPU model training

Transformer fine-tuning

MLflow

Python engineering

SQL processing

CI/CD

Automated testing

ML/MLOps

Kubernetes

Enterprise environments

German proficiency

English proficiency

Tools

GitLab CI

DuckDB

Kubernetes

Jobbeschreibung

• 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 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


• German proficiency at B2+ level


• English proficiency at B1+ level

Core Competencies

Demonstrates expertise in building and orchestrating ML pipelines using Kubeflow, training models on GPUs, and fine-tuning transformers and LLMs. Proficient in Python engineering, SQL data processing, and maintaining engineering standards through CI/CD practices.

Highest-signal resume keywords

• Kubeflow Pipelines


• GPU Model Training


• Transformer Fine-Tuning


• MLflow


• Python Engineering

Hard Skills

• XGBoost


• CatBoost


• SQL


• CI/CD


• Automated Testing


• Data Processing


• ML/MLOps


• Clean Code


• Large Datasets


• Kubernetes

Industry Keywords

• Zero-Trust Environment


• Cloud-Native


• Enterprise Environments


• Regulated Environments

Tools & Technologies

• GitLab CI


• DuckDB