Machine Learning/AI Engineer

Who we are Crédit Agricole Corporate and Investment Banking (Crédit Agricole CIB) is the corporate and investment banking arm of Crédit Agricole Group, world’s 10th largest bank by total assets. Our Singapore center (“ISAP” or “Information Systems Asia Pacific”) is the 2nd largest IT setup (after Paris Head Office) for Crédit Agricole CIB's worldwide business. We work daily with international branches located in 30 markets by:

Envisioning and preparing the Bank’s futures information systemsPartnering and supporting core banking flagships and transverse areas in their large scale development projectsProviding premium In-house Banking applications This unique positioning empowers us to bring our core banking business a sustainable competitive advantage on the market. We seek innovative and agile people sharing our mindset to support ambitious and forthcoming technological challenges. Position In a challenging and multicultural environment, we are looking for a Machine Learning /AI Engineer to join our Digital Excellence Centre (DEC) department of Crédit Agricole CIB.

The department handles the development of transversal and international projects. We are looking for a seasoned Machine Learning Engineer with a strong background in data science and applied ML, who can design, build, and deploy end-to-end machine learning solutions. This hybrid role requires a hands-on expert who can not only build models when needed but also engineer scalable, production-grade ML systems while adhering to the organization's AI governance and compliance standards.

Main responsibilitiesCollaborate with data scientists and business stakeholders to understand use cases and define ML solution; work on Proof of Concepts wherever neededEngineer and deploy ML models into production using MLOps best practices (model versioning, monitoring, CI/CD, etc. ). Build & maintain data pipelines and model performance for scalability and maintainability. Ensure all models adhere to organizational AI policies, responsible AI practices, and audit requirements. Support data exploration, feature engineering, and occasional model building where needed. Automate model retraining, testing, and monitoring to ensure performance over time. Document ML workflows, governance checkpoints, and risk assessments.

Partner with DevOps, IT, and security teams to integrate solutions into enterprise platforms. The position requires autonomy and reliability in performing duties while maintaining close communication with rest of stake-holders. Qualifications and Profile5 years of experience in data science and machine learning, with at least 3+ years in ML engineering roles. Proven experience in end-to-end ML lifecycle: data wrangling, model development, deployment, and monitoring. Strong programming skills in Python (pandas, scikit-learn, TensorFlow/PyTorch, etc. ). Strong knowledge in NoSQL databases (any experience in Graph database is desirable)Experience with MLOps tools: MLflow, TFX, Airflow, Kubeflow, or similar.

Familiarity with cloud platforms (GCP, AWS, or Azure) for ML deployment. Knowledge of data science techniques including supervised/unsupervised learning, NLP, time series, etc. Experience with CI/CD pipelines and containerization (Docker, Kubernetes). Strong understanding of AI governance, model risk management, and regulatory requirements in AI. Ability to communicate technical concepts to non-technical stakeholders. Good to have: Experience with Responsible AI frameworks and bias/fairness testing. Exposure to feature stores, model registries, and data versioning. Knowledge of data privacy, anonymization, and compliance in regulated industries (e. g. , banking, healthcare).