Quantitative AI Portfolio Engineer (Fixed Income) H/F

Research & Quantitative Model Development

• Design and develop quantitative approaches and machine learning algorithms to generate investment signals on interest rates (curve dynamics, term premia, vol, macro linkages), and credit markets (spread dynamics, momentum, carry, liquidity, regime effects).
• Perform advanced feature engineering (macro, market microstructure, flows, liquidity, ESG, sentiment).
• Develop supervised and unsupervised learning models:
  • regression, classification, clustering
  • tree-based models, ensemble methods
  • deep learning architectures (LSTM, Transformers when relevant)
• Integrate alternative and unstructured data (news feeds, central bank communications, broker research, transcripts, regulatory publications).
• Build & maintain a reusable internal library of features, models, preprocessing pipelines, validation tools.
• Collaborate with the research ecosystem (e.g. Amundi Institute) to translate academic innovations into operational investment models.

Back-testing & Validation

• Implement and oversee robust back-testing frameworks addressing biases (look-ahead, survivorship), transaction costs, liquidity constraints and slippage.
• Perform deep robustness analysis via stress tests, walk-forward analysis, bootstrap methods and stability checks across market regimes, including crisis periods.
• Measure risk-adjusted performance of strategies and evaluate sensitivity to macro and market factors.
• Define clear model acceptance criteria, rejection thresholds and degradation metrics.

Engineering & Productionisation

• Own the full model lifecycle: specification, prototyping, validation, industrialization, monitoring and maintenance.
• Implement monitoring and alerting for data drift, model drift and performance decay, and define rollback procedures.

Reporting & Documentation

• Document methodologies, assumptions, validation metrics and production procedures.
• Prepare reports and presentations for senior management, investment committees and client-facing teams.
• Communicate model rationale, risks, limitations, and governance aspects clearly to non-technical stakeholders.
• Actively contribute to model governance, internal audits, regulatory reviews when applicable.