AI Engineer - Internship

3w3 weeks ago

Crédit Agricole Corporate and Investment Banking

Singapore, SG · Internship · S$24,000 – S$36,000

About this role

The AI Engineer Internship plays an exploratory, hands-on role within the AI Factory team. This position is dedicated to the research, prototyping, and experimentation of emerging GenAI capabilities across the Bank's business lines. Work alongside senior engineers in Crédit Agricole CIB's Singapore center, the 2nd largest IT setup after Paris.

Evaluate cutting-edge architectures such as multi-agent workflows and different retrieval-augmented generation (RAG) strategies. Build rapid prototypes and Proof of Concepts (PoCs) to test ideas. Translate ambiguous business problems into testable AI prototypes while maintaining a sharp, open mind.

Crédit Agricole CIB's ISAP works daily with international branches in 30 markets. Envision and prepare the Bank's future information systems. Partner and support core banking flagships and transverse areas in large-scale development projects.

This environment is optimized for experimentation and discovering high-value technological applications. Seek innovative and agile people sharing our mindset to support ambitious technological challenges. Document outcomes and present findings on the latest GenAI developments.

Requirements

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
Proficiency in Python for AI development
Experience with AI coding assistants like GitHub Copilot

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 for banking use cases
Bridge Business & Tech by interpreting business pain points and translating them into experimental AI solutions with team lead and senior engineers
Collaborate with core engineering team to map out integration of successful prototypes into production-grade deployments
Document experimentation outcomes, build internal knowledge bases, and present findings on latest GenAI developments
Continuously leverage AI coding tools to accelerate the prototyping cycle and test new ideas efficiently