Sr AI Engineer / Sr. Data Scientist - to join ASAP

Descartes & Mauss, Limited; Paris; Sur place; EUR 80 000 - 120 000; Plein temps

Résumé du poste

Descartes & Mauss, Limited is seeking a senior AI leader based in Paris to define and execute the technical strategy for AI algorithms. The role involves leading teams, designing AI pipelines, and ensuring the scalability and robustness of AI components. Candidates should have over 5 years of experience in Machine Learning and NLP, strong Python skills, and familiarity with RAG and LLMOps tools. This dynamic position offers a unique opportunity to innovate in the AI space.

Qualifications

• 5+ years of experience in Machine Learning / NLP / LLMs.
• Solid understanding of RAG & LLMOps concepts and related tools.
• Strong Python skills with backend/data architecture knowledge.

Responsabilités

• Define and drive the AI strategy and technical roadmap.
• Lead and grow the team including mentoring and establishing best practices.
• Design and industrialize AI pipelines and orchestration of AI agents.
• Ensure the robustness and scalability of AI components.

Connaissances

Machine Learning

NLP

AI Strategy

Python

RAG

LLMOps

Agile

Technical Leadership

Outils

PyTorch

Transformers

Hugging Face

FastAPI

Airflow

Spark

Description du poste

You will be responsible for the technical vision and implementation of the AI algorithms that enable the copilot to understand, reason, and reliably answer users’ business-related questions.

You will work hand-in-hand with the other tech leads and product leads to define and execute the product’s overall technical strategy.

• Define and drive the AI strategy: design the technical roadmap around RAG, semantic search, language models (LLMs), agent orchestration (LangGraph, etc.), and answer quality.

• Lead and grow the team (MLEs, data scientists, data engineers): pair programming, code reviews, mentoring, hiring, and establishing best practices.

• Design and industrialize AI pipelines: ingestion, vectorization, indexing, fine-tuning, evaluation, and model monitoring.

• Strong understanding of LLM behavior, prompt‑engineering techniques, and strategies for orchestrating multi‑step AI agents

• Ability to connect AI agents with external tools, APIs, databases, and automation platforms to enable end‑to‑end workflow execution

• Experience designing and implementing agentic workflows, including task decomposition, tool integration, and autonomous decision‑making logic.

• Proficiency in monitoring, evaluating, and optimizing agent performance, including error handling, memory management, and iterative refinement.

• Ensure the robustness and scalability of AI components, working closely with the platform/backend team.

• Collaborate with Product team across squads to turn business requirements into effective technical solutions.

• Conduct continuous technology watch: stay at the forefront of RAG, LLMOps, evaluation frameworks, agents, and multimodality.

What We Expect From You

• You can switch easily between strategic and hands‑on work: architect an AI system one day, and optimize a pipeline or model the next.

• You know how to balance delivery speed with technical quality.

• You can communicate clearly with both technical and non‑technical stakeholders.

• You drive the adoption of best practices (testing, CI/CD, documentation, monitoring).

• You foster a strong culture of collaboration and feedback.

• You are comfortable in an agile environment (Scrum, squads, sprints, rituals).

Preferred experience

• Solid experience (5+ years) in Machine Learning / NLP / LLMs, including significant work on production‑grade projects.
• Strong command of RAG & LLMOps concepts and tools: vector DBs, retrievers, embeddings, evaluation, LangChain/LangGraph, agent orchestration, etc.
• Excellent knowledge of ML frameworks: PyTorch, Transformers, Hugging Face, etc.
• Strong Python skills and good understanding of backend/data architectures (FastAPI, Airflow, Spark, etc.).
• Experience deploying models to production, ML CI/CD, monitoring, and performance.
• Technical leadership abilities: mentoring, code reviews, spreading best practices, cross‑squad coordination.
• Curiosity, pragmatism, and a passion for real‑world innovation.
• Experience with LLM evaluation frameworks.
• Participation in open‑source projects or public AI contributions.