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Machine Learning/AI Engineer Job Opening In Singapore, Singapore – Now Hiring Crédit Agricole CIB


Job description

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

#J-18808-Ljbffr

Required Skill Profession

Electrical & Energy Engineering


  • Job Details

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Unlock Your Machine Learning Potential: Insight & Career Growth Guide


Real-time Machine Learning Jobs Trends (Graphical Representation)

Explore profound insights with Expertini's real-time, in-depth analysis, showcased through the graph here. Uncover the dynamic job market trends for Machine Learning in Singapore, Singapore, highlighting market share and opportunities for professionals in Machine Learning roles.

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Are You Looking for Machine Learning/AI Engineer Job?

Great news! is currently hiring and seeking a Machine Learning/AI Engineer to join their team. Feel free to download the job details.

Wait no longer! Are you also interested in exploring similar jobs? Search now: .

The Work Culture

An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at Crédit Agricole CIB adheres to the cultural norms as outlined by Expertini.

The fundamental ethical values are:

1. Independence

2. Loyalty

3. Impartiapty

4. Integrity

5. Accountabipty

6. Respect for human rights

7. Obeying Singapore laws and regulations

What Is the Average Salary Range for Machine Learning/AI Engineer Positions?

The average salary range for a varies, but the pay scale is rated "Standard" in Singapore. Salary levels may vary depending on your industry, experience, and skills. It's essential to research and negotiate effectively. We advise reading the full job specification before proceeding with the application to understand the salary package.

What Are the Key Qualifications for Machine Learning/AI Engineer?

Key qualifications for Machine Learning/AI Engineer typically include Electrical & Energy Engineering and a list of qualifications and expertise as mentioned in the job specification. The generic skills are mostly outlined by the . Be sure to check the specific job listing for detailed requirements and qualifications.

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Interview Tips for Machine Learning/AI Engineer Job Success

Crédit Agricole CIB interview tips for Machine Learning/AI Engineer

Here are some tips to help you prepare for and ace your Machine Learning/AI Engineer job interview:

Before the Interview:

Research: Learn about the Crédit Agricole CIB's mission, values, products, and the specific job requirements and get further information about

Other Openings

Practice: Prepare answers to common interview questions and rehearse using the STAR method (Situation, Task, Action, Result) to showcase your skills and experiences.

Dress Professionally: Choose attire appropriate for the company culture.

Prepare Questions: Show your interest by having thoughtful questions for the interviewer.

Plan Your Commute: Allow ample time to arrive on time and avoid feeling rushed.

During the Interview:

Be Punctual: Arrive on time to demonstrate professionalism and respect.

Make a Great First Impression: Greet the interviewer with a handshake, smile, and eye contact.

Confidence and Enthusiasm: Project a positive attitude and show your genuine interest in the opportunity.

Answer Thoughtfully: Listen carefully, take a moment to formulate clear and concise responses. Highlight relevant skills and experiences using the STAR method.

Ask Prepared Questions: Demonstrate curiosity and engagement with the role and company.

Follow Up: Send a thank-you email to the interviewer within 24 hours.

Additional Tips:

Be Yourself: Let your personality shine through while maintaining professionalism.

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Turn Off Phone: Avoid distractions during the interview.

Final Thought:

To prepare for your Machine Learning/AI Engineer interview at Crédit Agricole CIB, research the company, understand the job requirements, and practice common interview questions.

Highlight your leadership skills, achievements, and strategic thinking abilities. Be prepared to discuss your experience with HR, including your approach to meeting targets as a team player. Additionally, review the Crédit Agricole CIB's products or services and be prepared to discuss how you can contribute to their success.

By following these tips, you can increase your chances of making a positive impression and landing the job!

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