AI Engineer

Role overview

Responsibilities

• check_circle Design, build, and ship machine learning and generative AI features
• check_circle Build LLM-powered solutions: prompt design, embeddings, RAG pipelines, and integrations with APIs such as OpenAI, Anthropic, or open-source models
• check_circle Take models from prototype to production: APIs (e.g., FastAPI), Docker, cloud deployment, and monitoring
• check_circle Measure and improve model performance, output quality, cost, and latency; run A/B tests where relevant
• check_circle Collaborate closely with the Data Engineer on datasets, features, and data quality
• check_circle Keep up with the fast-moving AI landscape and propose new product opportunities

Basic qualifications

• 2–5 years of professional experience in machine learning, data science, or AI engineering
• Strong Python skills and experience with the ML ecosystem (scikit-learn, plus PyTorch or TensorFlow)
• At least one ML or AI feature shipped to production, end to end
• Hands-on experience with LLMs: prompting, embeddings, and RAG (LangChain, LlamaIndex, or similar)
• Solid SQL and data manipulation skills (pandas)
• Working knowledge of Docker, Git, and at least one cloud platform (AWS, GCP, or Azure)
• Degree in computer science, data science, or engineering, or equivalent practical experience
• Professional working proficiency in French and English