AI Engineer Professional Certification – Level 7 RNCP (Master’s degree equivalent)

09/2025-08/2026

Institution

OpenClassrooms

Diploma

Level 7 professional certification (BAC+5) — Data Science & Engineering Expert (RNCP39775)

Overview

This master's-level professional program prepares graduates to design, develop, and deploy AI solutions in production. Built around concrete projects inspired by real business challenges, it covers the full lifecycle of an AI project: data collection and preparation, Machine Learning and Deep Learning model development, MLOps, cloud deployment, monitoring, generative AI, RAG, and intelligent agents.

The program is fully remote, with one-on-one mentoring from an expert and the completion of fifteen hands-on professional projects.

Skills acquired

• Designing and leading artificial intelligence projects.
• Designing robust, automated data pipelines.
• Developing Machine Learning and Deep Learning models.
• Designing Computer Vision and multimodal processing systems.
• Developing solutions based on LLMs, RAG, and AI agents.
• Deploying models to production following MLOps principles.
• Setting up CI/CD pipelines and monitoring models.
• Optimizing, fine-tuning, and evaluating AI models.
• Designing APIs and demo applications around AI models.
• Industrializing Data and AI workflows in cloud environments.

Core curriculum

• Advanced Python
• SQL and PostgreSQL
• Machine Learning
• Deep Learning
• Computer Vision
• Multimodal processing
• Generative AI (LLMs)
• Retrieval-Augmented Generation (RAG)
• Language model fine-tuning (SFT, DPO)
• Reinforcement Learning
• Data Engineering
• MLOps
• Cloud deployment
• Monitoring and observability
• AI project management

Technologies covered

• Python
• SQL
• PostgreSQL
• Pandas
• scikit-learn
• PyTorch
• MLflow
• FastAPI
• BentoML
• Streamlit
• LangChain
• Airbyte
• PySpark
• Kestra
• Great Expectations
• Git
• GitHub
• Docker
• CI/CD
• Pydantic
• Pytest
• Cloud Computing

Projects completed

• Developed services using AI models via API.
• Built an energy consumption forecasting model.
• Automated data classification using supervised models.
• Deployed and monitored a Machine Learning model in production.
• Designed a RAG system using vector databases and LLMs.
• Implemented a semi-supervised approach for computer vision.
• Developed a Reinforcement Learning agent.
• Extracted multimodal data from websites.
• Developed AI agents capable of using external tools.
• Fine-tuned a language model (LLM) with LoRA, QLoRA, SFT, and DPO.
• Delivered a full scoping project for an AI solution.
• Built a professional portfolio showcasing all completed projects.

Key achievements

• Completed 15 hands-on professional projects covering the full lifecycle of an AI project.
• Deployed several AI models to production using MLOps practices (testing, CI/CD, monitoring, and observability).
• Developed solutions based on LLMs, RAG, AI agents, and open-source model fine-tuning.
• Gained expertise spanning Data Engineering, Machine Learning, Deep Learning, and generative AI.
• Delivered projects using cloud environments, REST APIs, and modern deployment architectures.