ML/MLOps Engineer in Germany

HeadHR; München; Vor Ort; EUR 95.000 - 140.000; Vollzeit

Zusammenfassung

Andersen in Germany is seeking an ML/MLOps Engineer to build a cloud-native AI platform for healthcare. You will design and run ML pipelines with Kubeflow, train and fine-tune models on GPUs, and track experiments with MLflow in a secure, zero-trust environment.

Required are 5+ years in MLOps/ML engineering, Kubeflow Pipelines, GPU training, and proficiency in Python; German at upper-intermediate level is required.

Qualifikationen

• 5+ years of MLOps/ML engineering experience.
• Hands-on with Kubeflow Pipelines (KFP v2).
• Experience training models on GPUs.
• Experience fine-tuning LLMs/transformers.
• Experience with MLflow (model tracking).
• Experience with boosting models (XGBoost, CatBoost).
• Deep proficiency in Python and modern engineering practices.
• Experience in regulated/enterprise cloud-native environments.
• Experience with SQL and large-scale data processing.
• CI/CD experience (GitLab CI).
• English – Intermediate+; German – Upper-Intermediate+.

Aufgaben

• Build and orchestrate ML pipelines using Kubeflow Pipelines (KFP v2).
• Train models on GPUs, including GPU resource management in Kubernetes.
• Fine-tune transformers/LLMs and track experiments with MLflow.
• Build classic ML models (XGBoost, CatBoost).
• Work with data using SQL Server and DuckDB for large datasets.
• Develop in Python for pipelines, integrations, tooling.
• Ensure code quality: testing, CI/CD (GitLab CI).
• Operate in a zero-trust/secure-by-default environment.

Kenntnisse

Python

Kubernetes

CI/CD

English

German

ML engineering

Data processing

Tools

Kubeflow Pipelines

MLflow

XGBoost

CatBoost

SQL Server

DuckDB

GitLab CI

Kubernetes

Jobbeschreibung

Andersen is hiring an ML/MLOps Engineer in Germany for a project building a cloud-native AI platform and delivering scalable machine learning solutions for the healthcare industry.

The project is focused on building a cloud-native MLOps platform for the healthcare sector to support large-scale machine learning and AI workloads. It includes developing ML infrastructure based on Kubeflow, enabling LLM fine-tuning, traditional machine learning, and scalable data processing in a secure, zero-trust environment.

Responsibilities:

• Building and orchestrating ML pipelines using Kubeflow Pipelines (KFP v2).
• Training models on GPUs, including GPU resource management within Kubernetes.
• Fine-tuning transformers/LLMs, tracking experiments and models via MLflow.
• Building classic ML models (XGBoost, CatBoost).
• Working with data using SQL Server and DuckDB as a lightweight OLAP solution for efficient in-cluster processing of large datasets.
• Developing in Python (pipelines, integrations, tooling based on uv).
• Ensuring code quality: testing, CI/CD (GitLab CI).
• Working within a zero-trust / secure-by-default environment (network policies, restrictive container rights).

Kogo poszukujemy?

Must-haves:

• Experience as a MLOps Engineer / ML Engineer for 5+ years.
• Hands-on experience with Kubeflow Pipelines (KFP v2).
• Experience training models on GPUs.
• Experience fine-tuning LLMs/transformers.
• Experience with MLflow (model tracking).
• Experience with boosting models (XGBoost, CatBoost).
• Deep proficiency in the Python ecosystem and modern engineering practices.
• Experience working in regulated/enterprise cloud-native environments.
• Experience with SQL and large-scale data processing.
• CI/CD experience (GitLab CI preferred), clean code and testing practices.
• Level of English – from Intermediate+ or above.
• Level of German – from Upper-Intermediate or above.

Nice-to-haves:

• Pre-training experience for LLMs (beyond fine-tuning).
• Experience with GPU orchestration in Kubernetes.
• Experience in zero-trust environments (network policies, restrictive container rights).
• Knowledge of DuckDB.
• Experience with modern Python tooling (uv).