Beschreibung
Job Title: Freelance Machine Learning Engineer – Healthcare AI
Location: Remote (Client based in Germany)
Contract Type: Freelance
Duration: 6 months (extension likely)
Language: English (German a plus)
About the Role
We are supporting a leading healthcare client in Germany in their search for a freelance Machine Learning Engineer to help develop scalable AI systems for diagnostic support and clinical insights. You’ll work with healthcare data at scale and contribute to intelligent solutions that enhance patient outcomes and medical efficiency.
Responsibilities
- Design, train, and optimize ML models using structured and unstructured healthcare data
- Build scalable pipelines for data ingestion, training, validation, and deployment
- Collaborate with data scientists, product teams, and medical advisors to understand real-world requirements
- Ensure compliance with GDPR and medical data regulations
- Implement monitoring and logging for model performance in production
- Contribute to architectural decisions around ML infrastructure and tooling
Requirements
- 4+ years of experience in machine learning engineering, preferably with healthcare or regulated data
- Expert knowledge of Python and ML frameworks (e.g. TensorFlow, PyTorch, Scikit-learn)
- Experience with MLOps workflows including versioning, reproducibility, and deployment
- Familiarity with Docker, Kubernetes, CI/CD, and model serving tools
- Solid background in data engineering: ETL pipelines, batch + streaming data (e.g., Apache Airflow, Spark)
- Cloud experience with AWS, GCP, or Azure, especially for ML training and inference
- Strong understanding of model evaluation, bias, and interpretability
- Experience with medical imaging (DICOM) or EHR datasets is a major plus
- Fluent in English; strong documentation and collaboration skills
Preferred Tech Stack
- Languages & Frameworks: Python, SQL, TensorFlow, PyTorch, Scikit-learn
- Data Pipelines: Apache Airflow, Prefect, Spark
- MLOps: MLflow, DVC, Kubeflow, TFX
- DevOps: Docker, Kubernetes, GitHub Actions
- Cloud: AWS SageMaker, GCP Vertex AI, Azure ML Studio
- Other Tools: FastAPI, RESTful APIs, Streamlit, Weights & Biases
Location: Remote (Client based in Germany)
Contract Type: Freelance
Duration: 6 months (extension likely)
Language: English (German a plus)
About the Role
We are supporting a leading healthcare client in Germany in their search for a freelance Machine Learning Engineer to help develop scalable AI systems for diagnostic support and clinical insights. You’ll work with healthcare data at scale and contribute to intelligent solutions that enhance patient outcomes and medical efficiency.
Responsibilities
- Design, train, and optimize ML models using structured and unstructured healthcare data
- Build scalable pipelines for data ingestion, training, validation, and deployment
- Collaborate with data scientists, product teams, and medical advisors to understand real-world requirements
- Ensure compliance with GDPR and medical data regulations
- Implement monitoring and logging for model performance in production
- Contribute to architectural decisions around ML infrastructure and tooling
Requirements
- 4+ years of experience in machine learning engineering, preferably with healthcare or regulated data
- Expert knowledge of Python and ML frameworks (e.g. TensorFlow, PyTorch, Scikit-learn)
- Experience with MLOps workflows including versioning, reproducibility, and deployment
- Familiarity with Docker, Kubernetes, CI/CD, and model serving tools
- Solid background in data engineering: ETL pipelines, batch + streaming data (e.g., Apache Airflow, Spark)
- Cloud experience with AWS, GCP, or Azure, especially for ML training and inference
- Strong understanding of model evaluation, bias, and interpretability
- Experience with medical imaging (DICOM) or EHR datasets is a major plus
- Fluent in English; strong documentation and collaboration skills
Preferred Tech Stack
- Languages & Frameworks: Python, SQL, TensorFlow, PyTorch, Scikit-learn
- Data Pipelines: Apache Airflow, Prefect, Spark
- MLOps: MLflow, DVC, Kubeflow, TFX
- DevOps: Docker, Kubernetes, GitHub Actions
- Cloud: AWS SageMaker, GCP Vertex AI, Azure ML Studio
- Other Tools: FastAPI, RESTful APIs, Streamlit, Weights & Biases