Who we are

Crédit Agricole Corporate and Investment Bank (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 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.

Position

We are seeking a skilled AI Engineer with strong experience in building, deploying, and optimizing AI solutions within enterprise infrastructure environments.

The ideal candidate will have at least 2 years of hands-on experience in AI/ML development, a solid understanding of server, storage, and network security, and the ability to design intelligent systems such as chatbots and automation frameworks.

This role requires a deep understanding of AI algorithms, modern machine learning frameworks, and accelerated computing technologies.

Main responsibilities

AI/ML Development

• Design, develop, and optimize machine learning models using Python/RUST frameworks.
• Build and maintain AI-powered applications, including chatbots, intelligent assistants, and automation tools. Implement and fine-tune deep learning models for NLP, computer vision, or data analytics tasks.
• Develop algorithms and data pipelines for scalable AI training and inference

Infrastructure Engineering

• Work closely with infrastructure teams to deploy and optimize AI workloads on on-prem or cloud-based servers.
• Configure, manage, and monitor GPU-accelerated systems (CUDA) to support model training and inference.
• Ensure AI applications integrate efficiently with storage systems, networking, and security policies.
• Assist in performance benchmarking, load testing, and capacity planning for AI platforms.

Security & Compliance

• Implement security best practices for AI systems, data pipelines, and APIs.
• Collaborate with network and security teams to maintain compliance and safeguard sensitive data.
• Ensure safe and responsible deployment of AI models and services.

Legal and Regulatory Responsibilities:

• Comply with all applicable legal, regulatory and internal Compliance requirements, including, but not limited to, the Singapore Compliance manual and Compliance policies and procedures as issued from time to time; Financial Security requirements, including, but not limited to, the prevention of Financial Crime and Fraud including reporting obligations to the Money Laundering Reporting Officer/Compliance Officer.
• Maintain appropriate knowledge to ensure to be fully qualified to undertake the role. Complete all mandatory training as required to attain and maintain competence.

Qualifications and Profile

• Minimum 2 years of hands-on experience in AI and machine learning development.
• Strong knowledge of Python, AI modules like PyTorch, Keras and key AI/ML algorithms.
• Experience with GPU/CUDA programming, GPU optimization, or distributed training.
• Understanding of server, storage, and networking concepts, including infrastructure security.
• Experience developing chatbots (NLP-based or LLM-based).
• Familiarity with data engineering and model deployment workflows (CI/CD, API development, containerization).
• Excellent communication skills.
• Knowledge of DevOps, MLOps, or infrastructure automation tools.
• Familiarity with vector databases, RAG pipelines, or LLM fine-tuning.
• Experience working with enterprise-grade GPU servers.
• Using / Deploy AI orchestration platforms (CrewAI, n8n, etc.) layered with MCP
• Leveraging with RAG ecosystems:
• LangChain, LlamaParse, etc.
• Vector DBs : FAISS, Redis Vector Store
• Embeddings Models
• CI/CD and benchmarking the opensource LLMs
• STT: Nice to have an experience on a on-prem Speech to text for focused activities.
• Experience with designing an end-to-end AI solution architecture at an enterprise level