AI Engineer

About the role

CMA CGM is seeking an AI Engineer with 5‑8 years of experience to design, build and deploy intelligent systems that support operational innovation. The role bridges AI, cloud infrastructure and software engineering, focusing on production‑grade solutions from LLM‑powered back‑ends to interactive web applications.

Key responsibilities

• Design and develop AI/ML pipelines and Retrieval‑Augmented Generation (RAG) systems using large language models.
• Build and maintain backend services in Python, especially FastAPI, Asyncio and asynchronous programming patterns.
• Develop and orchestrate AI agents with frameworks such as LangChain, Langgraph, Google Vertex AI Agent Builder or Google ADK.
• Containerize applications using Docker and manage deployments on cloud infrastructure.
• Design and interact with SQL (PostgreSQL) and NoSQL (MongoDB) databases.
• Create proof‑of‑concept web applications with Gradio or Streamlit and production‑grade user interfaces with React.
• Connect to SharePoint and Microsoft 365 services via the Microsoft Graph API.
• Deploy and manage application releases using XL Deploy.
• Collaborate with cross‑functional teams while maintaining code quality through GitLab or GitHub CI/CD pipelines.

Required profile

• 5–8 years of professional experience in software, backend, cloud or ML engineering.
• At least 2 years of hands‑on experience in artificial intelligence, including building production‑grade AI solutions.
• Proven ability to deliver scalable applications and AI agents.

Required skills

• Python (FastAPI, Asyncio, asynchronous programming)
• Docker and containerized deployment
• Cloud platforms – Azure or Google Cloud Platform
• XL Deploy
• PostgreSQL – schema design and query optimization
• MongoDB – document modeling and aggregation pipelines
• REST API design with OAuth 2.0, API Keys, JWT and OpenAPI/Swagger documentation
• Microsoft Graph API for SharePoint and Microsoft 365 integration
• Gradio or Streamlit for rapid prototyping
• React for production‑grade user interfaces
• GitLab or GitHub – branching strategies, merge requests and CI/CD pipelines
• Large language models (OpenAI, Gemini, Claude or open‑source) and prompt engineering
• Retrieval‑Augmented Generation pipelines, vector stores and document ingestion
• Agent frameworks such as LangChain, Langgraph, Google ADK or Google Vertex AI Agent Builder