🧠 AI ENGINEER (M/F)

AI Engineers design, develop, and deploy artificial intelligence applications, particularly those built on large language models (LLMs): assistants, autonomous agents, RAG systems, and intelligent automations. This is the flagship role of the generative AI wave, at the intersection of software engineering and machine learning.

🎯 Tasks

• Design and implement end-to-end AI features: from prototype to production.
• Build reliable RAG systems and agents (tools, memory, safeguards).
• Rigorously evaluate models: evaluation pipelines, accuracy, bias, and regression.
• Optimizing production: latency, caching, inference costs, scalability.
• Ensure security, privacy, and compliance (AI Act, GDPR).
• Monitor an ecosystem that evolves every month.

🛠️ Skills & Tech Stack 2026

• Languages: Python (essential), TypeScript frequently used on the product side.
• Models: OpenAI, Anthropic, Mistral, and Gemini APIs, plus open-source (Llama) via vLLM.
• GenAI stack: RAG, vector databases, agent frameworks, fine-tuning, prompt engineering.
• Evaluation & MLOps: evaluation pipelines, monitoring, CI/CD, traceability.
• ML/NLP Fundamentals: Hugging Face, PyTorch, and the basics of deep learning.

💰 2026 Salaries (as reported in France)

• Junior: 50–60 k€
• Confirmed: 60–85 k€
• Senior / Lead: 85–130 k€ and up (often with equity in AI startups)
• Freelance: Average Daily Rate (ADR) €700–1,200 — the most dynamic segment of the market.

🔍 Not to be confused with

The Data Scientist (modeling and experimentation) and the ML Engineer (training and serving infrastructure): the AI Engineer builds the applications that utilize the models.

❓ Frequently Asked Questions

Do you need a Ph.D. to become an AI engineer? No: a strong background in software engineering with hands-on experience with LLMs (projects, delivered products) is the most sought-after profile.

How do you evaluate an AI Engineer in an interview? Through real-world scenarios: designing a RAG, evaluation strategies, and trade-offs between cost, latency, and quality—not just based on knowledge of the latest models.

📈 Trends

Lead AI Engineer, AI Architect, Head of AI, CTO.