Finance ML Engineer: End-to-End Models & MLOps

Siemens Energy; Amadora; Presencial; EUR 60 000 - 90 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Flexible work options

Remote work available

Employee pension

Resumo da oferta

Siemens Energy is seeking a Machine Learning Engineer (m/f/d) to design, deploy, and scale AI/ML solutions for finance use cases. You will own end-to-end model lifecycle from data ingestion to monitoring, emphasize time-series forecasting, and implement robust MLOps for production systems.

Join a team delivering production-grade APIs and features, collaborating with stakeholders while mentoring colleagues. A quantitative advanced degree and hands-on experience with Python, PyTorch/TensorFlow are

Qualificações

• Advanced degree in a quantitative field.
• Hands-on experience deploying production-grade AI/ML solutions.
• Experience applying AI/ML in finance is a strong advantage.
• Strong Python and ML framework proficiency (PyTorch, TensorFlow, Scikit-Learn).

Responsabilidades

• Design, develop, validate, and deploy end-to-end AI/ML solutions for finance use cases.
• Lead the full model lifecycle: data ingestion, model selection, training, evaluation, versioning, monitoring.
• Run experiments and backtests for time-series models.
• Ensure models meet accuracy, scalability, governance, and responsible AI standards.
• Engineer features from internal/external data and signals.
• Implement MLOps with monitoring, drift detection, retraining triggers.
• Deploy production-grade APIs and collaborate with stakeholders, mentoring team members.

Conhecimentos

Python

MLOps

Feature engineering

Time-series forecasting

SQL

Agile

Formação académica

Master's or PhD in a quantitative field

Ferramentas

PyTorch

TensorFlow

Scikit-Learn

API development

Descrição da oferta de emprego

Siemens Energy is seeking a Machine Learning Engineer (m/f/d) to design, deploy, and scale AI/ML solutions for finance use cases. You will own end-to-end model lifecycle from data ingestion to monitoring, emphasize time-series forecasting, and implement robust MLOps for production systems.

Join a team delivering production-grade APIs and features, collaborating with stakeholders while mentoring colleagues. A quantitative advanced degree and hands-on experience with Python, PyTorch/TensorFlow are