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 systems
Partnering and supporting core banking flagships and transverse areas in their large scale development projects
Providing 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 responsibilities
Collaborate with data scientists and business stakeholders to understand use cases and define ML solution; work on Proof of Concepts wherever needed
Engineer 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 Profile
5 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).
Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Information Technology
Industries
Banking, Financial Services, and Technology, Information and Media
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