(Internship Jul 26) DEC/AI Factory - 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 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

The AI Engineer Internship plays an exploratory, hands-on role within the AI Factory team. Unlike a traditional production-focused engineering role, this position is dedicated to the research, prototyping, and experimentation of emerging GenAI capabilities across the Bank's business lines.

The incumbent will work alongside senior engineers to evaluate cutting-edge architectures, such as multi-agent workflows and different retrieval-augmented generation (RAG) strategy, and build rapid prototype / Proof of Concepts (PoCs). A core expectation is the ability to maintain a sharp, open mind, translating ambiguous business problems into testable AI prototypes. This is an environment optimized for experimentation and discovering high-value technological applications.

Main Responsibilities

• Rapidly develop AI applications and PoCs using LLMs, RAG systems, and AI agents with Python and AI coding assistants (GitHub Copilot).
• Research, benchmark, and test emerging AI frameworks, models, and architectures to determine their applicability for banking use cases.
• Bridge Business & Tech: Work with the team lead and senior engineers to interpret business pain points and translate them into experimental AI solutions.
• Collaborate on Integration: Partner with the core engineering team to map out how successful prototypes can eventually be packaged and transitioned into production-grade deployments.
• Knowledge Sharing: Document experimentation outcomes, build internal knowledge bases, and present findings on the latest GenAI developments to the wider team.
• AI-Native Development: Continuously leverage AI coding tools to accelerate the prototyping cycle and test new ideas efficiently.

Qualifications and Profile

Education

• Bachelor's or Master's degree in Computer Science, Data Science, Information Systems or a related engineering quantitative discipline.

Experience

• Penultimate or final year students, and recent graduates (within 12 months of graduation).
• Hands-on exposure to AI/ML concepts through academic projects, internships, personal projects, or hackathons.
• Demonstrable familiarity with LLM APIs, prompt engineering, or RAG concepts — formal work experience is not required; evidence of self-directed learning is valued equally.
• Practical experience with Python and at least one cloud platform (AWS, Azure, or GCP).
• Use of AI-assisted development tools: GitHub Copilot or equivalent (e.g., Cursor, Antigravity etc)
• Understanding of RESTful APIs and basic software development principles
• Knowledge of version control (Git/GitHub)
• Exposure to ETL tools, data pipelines, or containerization (Docker/Kubernetes) is a plus
• ReactJS experience is a bonus
   

Other Professional Skills and Mind-set

• AI-native development mindset – treats AI coding assistants (GitHub Copilot or equivalent) as a major development instrument; instinctively uses AI tools to compress cycle time from specification to working prototype, and continuously looks for ways to amplify personal output through intelligent tool use.
• Strong analytical and problem-solving skills applied to complex, ambiguous business challenges.
• Proactive communication - comfortable asking clarifying questions and surfacing issues early.
• Genuine curiosity about AI developments and a track record of self-directed learning.
• Team player with the ability to work independently when required, in a multicultural and geographically distributed environment.
• Basic understanding of financial services or banking concepts, or a strong willingness to develop this knowledge rapidly.