Machine Learning Engineer

As a Machine Learning Engineer (m/f/d), you are essential for developing, operationalizing, and scaling machine-learning models for finance use cases, aiming to convert data into reliable predictions and support AI-driven decision-making. To achieve this, the role is structured around three core areas: end-to-end AI/ML solution development, specialized data and feature engineering for financial forecasting, and the implementation of robust MLOps for production systems. This focus ensures the creation of scalable, governable, and impactful AI solutions that directly address business problems in the finance domain. Given the focus on advanced model development and productionalization, the ideal candidate will be an expert professional with an advanced degree in a quantitative field. They will possess significant hands-on experience in deploying predictive AI solutions, proficiency in Python and relevant ML frameworks, and a strong understanding of MLOps practices and financial data. How You'll Make an Impact

• Design, develop, validate, and deploy end-to-end AI/ML solutions for finance use cases such as forecasting, anomaly detection, and optimization.
• Lead the full model lifecycle, including data ingestion, model selection, training, evaluation, versioning, and performance monitoring.
• Run controlled experiments and backtests to quantify incremental value and ensure robustness, particularly for time-series-based models.
• Ensure all AI/ML solutions meet standards for accuracy, scalability, governance, and responsible AI practices.
• Engineer high-quality features and predictive signals from internal data, external sources, and LLM-derived inputs.
• Apply dimensionality reduction and advanced feature selection to build compact, interpretable, and high-performing predictors.
• Implement MLOps practices such as automated monitoring, drift detection, retraining triggers, and rollback strategies for production models.
• Deploy production-grade APIs and services and collaborate with stakeholders to translate business problems into measurable AI/ML outcomes while mentoring team members., * The professional and personal development of our employees is very important to us. We provide them with the opportunities to learn and develop in a self-determined way, various attractive programmes and learning materials are available for this purpose
• In relation to the "compatibility of family and work", we have a wide range of offers, e.g. flexible working time models, childcare places at many locations, the possibility of trial part-time work or even a sabbatical

• Advanced degree (Master's or PhD) in a quantitative field such as Computer Science, AI, Statistics, Applied Mathematics, or Econometrics.
• Hands-on professional experience developing and deploying production-grade AI/ML and predictive solutions.
• Experience applying AI/ML techniques in the finance domain is a strong advantage.
• Strong expertise in AI/ML development using Python with frameworks such as PyTorch, TensorFlow, and Scikit-Learn.
• Proven skills in feature engineering, signal engineering, and forecasting for predictive modeling.
• Solid background in MLOps and data engineering, including SQL, CI/CD pipelines, and API development.
• Experience operationalizing models through robust MLOps practices and scalable production workflows.
• Demonstrated thought leadership and the ability to work effectively within Agile development environments.

At Siemens Energy, we are more than just an energy technology company. With ~100.000 dedicated employees in more than 90 countries, we develop the energy systems of the future, ensuring that the growing energy demand of the global community is met reliably and sustainably. The technologies created in our research departments and factories drive the energy transition and provide the base for one sixth of the world's electricity generation., Find out how you can make a difference at Siemens Energy: https://www.siemens-energy.com/employeevideo