Machine Learning Engineer with German

Machine Learning Engineer

Responsibilities:

• Designing, training, and evaluating machine learning models (supervised, unsupervised, NLP, etc.).
• Building scalable data and ML pipelines using modern tools.
• Collaborating with subject matter experts and analysts to prepare training datasets.
• Deploying models for production (batch or real-time inference).
• Monitoring and maintaining model performance and data quality.
• Optimizing models for performance, interpretability, and cost.
• Documenting ML workflows and ensuring reproducibility.

Must-haves:

• Experience as a Machine Learning Engineer or in a similar role for 2+ years.
• Proficiency in Python, including hands-on experience with libraries such as scikit-learn, pandas, NumPy, and matplotlib.
• Strong understanding of core ML concepts – regression, classification, clustering, model validation, and performance metrics.
• Practical experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Proven experience building, training, and deploying ML models using AWS SageMaker.
• Familiarity with AWS Bedrock for working with foundation and generative models (e.g., fine-tuning and orchestration of LLMs).
• Hands-on experience with data preprocessing, feature engineering, and model evaluation.
• Knowledge of SQL and experience working with structured and semi-structured datasets.
• Understanding of ML model deployment (e.g., REST APIs with FastAPI or Flask; model packaging and containerization with Docker).
• Exposure to MLOps practices – pipeline automation, model versioning, monitoring, and reproducibility.
• Familiarity with version control systems (e.g., Git).
• Strong analytical thinking, communication, and problem-solving skills.
• Willingness to stay current with emerging ML techniques, frameworks, and cloud AI tools.
• Level of English – from Upper-Intermediate and above.
• Level of German – from Intermediate+ and above.