Quantitative AI Portfolio Engineer (Fixed Income) H/F

Amundi; Paris; Sur place; EUR 70 000 - 90 000; Plein temps

Résumé du poste

Amundi is seeking a Quantitative AI Portfolio Engineer to design and develop machine learning algorithms for investment signals in fixed income markets. This role demands collaboration with research teams and involves managing the full model lifecycle, from development to monitoring.

Ideal candidates will have an advanced degree in quantitative finance, 3-5 years of relevant experience, and strong programming skills in Python. The position is based in Paris and requires fluency in French and professional proficiency in English.

Qualifications

• 3-5 years of experience in quantitative research or data science applied to financial markets.
• Strong programming skills in Python and SQL.
• Familiarity with cloud platforms such as AWS or GCP.

Responsabilités

• Design and develop quantitative approaches and machine learning algorithms for investment signals.
• Own the full model lifecycle from specification to monitoring.
• Collaborate with the research ecosystem to implement innovations.

Connaissances

Machine learning techniques

Data science

Python programming

Quantitative research

NLP

SQL

Data engineering tools

Formation

Advanced degree in quantitative finance or equivalent

Outils

PyTorch/TensorFlow

Airflow

SQL

Description du poste

Missions

• 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: tree‑based models, ensemble methods, deep learning architectures (LSTM, Transformers when relevant).
• Integrate alternative and unstructured data such as news feeds, central bank communications, broker research, transcripts, regulatory publications.
• Build and maintain a reusable internal library of features, models, preprocessing pipelines and validation tools.
• Collaborate with the research ecosystem (e.g. Amundi Institute) to translate academic innovations into operational investment models.
• 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, industrialisation, monitoring and maintenance.
• Implement monitoring and alerting for data drift, model drift and performance decay, and define rollback procedures.
• 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.

Within the Fixed Income Investment Lab, the Quantitative AI Portfolio Engineer is responsible for the research, development, validation and deployment of machine learning‑driven investment models for fixed income portfolios.

The role focuses on end‑to‑end signal generation on rates and credit markets, from data engineering and feature construction to back‑testing, industrialisation and continuous monitoring.

The position sits at the intersection of quantitative research, applied AI and portfolio management, with the objective of transforming data into robust, explainable and actionable investment signals integrated into the daily decision‑making process of Portfolio Managers.

Paris

Qualifications

• Advanced degree in quantitative finance, financial engineering, applied mathematics, computer science, data science or equivalent.
• 3‑5 years of experience: significant experience (1+ to 6‑7 years) in quantitative research, data science applied to financial markets or systematic strategy development.
• Strong knowledge of modern machine learning techniques (neural networks, XGBoost, sequence models such as RNN/LSTM/Transformer, ensemble methods).
• Practical experience in NLP (Transformers (BERT), embeddings, fine‑tuning, clustering, classification and sentiment analysis) applied to financial text.
• Solid understanding of fixed‑income markets (yield curve structure, credit spreads, interest‑rate derivatives) and portfolio constraints.
• Strong programming skills in Python (Pandas, scikit‑learn, Hugging Face, spaCy, sentence‑transformers, PyTorch/TensorFlow, PySpark), SQL. C++/Java knowledge is a plus.
• Familiarity with data engineering tools (Airflow, Spark, Kafka) and cloud platforms (AWS/GCP/Azure) is advantageous.
• Scientific mindset, intellectual curiosity and experimental rigor.
• Ability to synthesize and explain technical results to non‑technical audiences.
• Autonomy, initiative and strong collaboration in cross‑functional teams.
• Production‑oriented mindset with focus on reproducibility and operational robustness.

Langues

Fluent in French and professional proficiency in English.