🤖 MACHINE LEARNING / DEEP LEARNING ENGINEER (M/F)

The Machine Learning/Deep Learning Engineer industrializes artificial intelligence: they transform experimental models into reliable, scalable systems that are monitored in production. At the intersection of software engineering and data science, this is one of the most sought-after roles in the market.

🎯 Tasks

• Scale up the training and deployment of models (traditional machine learning, deep learning, LLM).
• Building MLOps pipelines: CI/CD, model and data versioning, and drift monitoring.
• Optimizing Inference: Latency, Costs, GPUs, Quantization, High-Performance Serving.
• Fine-tune and evaluate LLMs; build robust RAG systems.
• Collaborate with data scientists, data engineers, and product teams.

🛠️ Skills & Tech Stack 2026

• Programming languages: Python (expert), and often Go/Rust/C++ for optimization.
• Frameworks: PyTorch (predominant), Hugging Face, JAX.
• MLOps: MLflow, Kubeflow, Weights & Biases, Airflow.
• Serving & GenAI: vLLM, Triton, TensorRT, vector databases, agent frameworks.
• Infrastructure: Docker, Kubernetes, cloud GPUs (AWS SageMaker, GCP Vertex AI, Azure ML).

💰 2026 Salaries (as reported in France)

• Junior: 48–58 k€
• Confirmed: 58–80 k€
• Senior / Lead: 80–120 k€ and up (higher compensation packages at AI scale-ups)
• Freelance: Average daily rate of €650–1,100.

🔍 Not to be confused with

The Data Scientist (exploration and modeling) andthe AI Engineer (LLM applications): the ML Engineer specializes in infrastructure and model deployment.

❓ Frequently Asked Questions

Deep learning or traditional ML? The two coexist: traditional ML (XGBoost, scikit-learn) remains the dominant approach for tabular data; deep learning dominates text, images, audio, and LLMs.

What is the number one requirement? To be an excellent software engineer: 80% of machine learning in production is engineering.

📈 Trends

ML Engineer, ML Architect, Head of ML/AI, AI CTO.