Senior AI Engineer

Location: Paris, Châtelet – Hybrid (2 days on-site / 3 days remote)

Découvrez exactement les compétences, l'expérience et les qualifications dont vous aurez besoin pour réussir dans ce rôle avant de postuler ci-dessous.

Contract: Full-time (CDI)

Team: Technology – Direct report to CTO

Main Mission

Identify, design, and deploy AI and Machine Learning solutions that concretely increase the value delivered by the Mendo application to our users. As a Senior Data Scientist / AI Engineer, you will be the technical pioneer of AI integration into our product, with full autonomy to explore, experiment, and implement ML and GenAI models (fine-tuning, embeddings, RAG, etc.). Your mission is to transform business opportunities into pragmatic and measurable AI solutions integrated in Mendo, serving over 60 enterprise clients (PwC Global, BDO, EY...). You will mentor a junior Data Scientist and establish the standards of excellence for data science practice at Mendo.

Key Responsibilities

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• Proactive AI Opportunity Identification: analyze the product, usage patterns, and customer feedback to identify where AI can create concrete value.
  
• Propose relevant AI use cases aligned with product roadmap and business objectives.
  
• Ruthlessly challenge and prioritize AI initiatives to focus on user impact and ROI.
  
• Collaborate closely with the Product Lead and PMs to transform business needs into actionable AI opportunities.
  
• Establish a framework for prioritizing AI projects based on impact, feasibility, and effort.
  
• Research and experiment with ML and GenAI models (LLMs, embeddings, classification, clustering, recommendation).
  
• Fine-tune existing models (OpenAI, Anthropic, open-source) rather than building from scratch.
  
• Design and implement end-to-end AI pipelines: data prep, training/fine-tuning, evaluation, deployment.
  
• Develop RAG (Retrieval-Augmented Generation), semantic search, and other GenAI architecture solutions.
  
• Optimize model performance and costs in production (latency, tokens, infrastructure).
  
• Implement monitoring and continuous quality evaluation systems for models.
  
• Prioritize ruthlessly, favor simple and effective solutions, avoid over-engineering, prototype fast, iterate, and document technical decisions.
  
• Mentor and train a junior Data Scientist; establish best practices for code quality, MLOps, experimentation; collaborate with development squads; evangelize AI possibilities internally; build the data science practice for future growth.
  
• Work hand-in-hand with the CTO, product teams, development teams, and Customer Success; present results and recommendations to stakeholders.
  

Required Skills

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• Machine Learning expertise: minimum 4–6 years of experience in model development and deployment.
  
• Deep mastery of LLM fine‑tuning (OpenAI, Anthropic, Llama, Mistral, etc.).
  
• Solid experience with GenAI architectures: RAG, embeddings, semantic search, advanced prompt engineering.
  
• Knowledge of ML/DL frameworks: PyTorch, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex.
  
• Experience with cloud platforms for ML (Azure ML, AWS SageMaker, or Google Vertex AI).
  
• Python mastery and data science libraries (scikit‑learn, pandas, numpy, etc.).
  
• Knowledge of vector databases (Pinecone, Weaviate, Qdrant, etc.).
  
• Familiarity with MLOps pipelines: model versioning, A/B testing, production monitoring.
  
• Understanding of cost, latency, and scalability challenges for AI solutions in production.
  

Know‑how

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• Demonstrated ability to prioritize effectively and deliver value quickly.
  
• Track record of AI use‑case proposals that created measurable business impact.
  
• End‑to‑end ownership from research to deployment and production monitoring.
  
• Ability to prototype rapidly and iterate based on user feedback.
  
• Excellence in model evaluation and benchmarking.
  
• Clear communication of complex technical concepts to non‑technical audiences.
  
• Close collaboration with product and engineering teams.
  
• Mentoring and training of junior profiles.
  

Soft Skills

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• Product mindset: obsession with user impact and real value creation.
  
• Autonomy and proactivity: identify opportunities without waiting to be told.
  
• Pragmatism and efficiency: preference for simple solutions that work.
  
• Sharp sense of priorities and courage to say no to non‑impactful projects.
  
• Insatiable curiosity: passion for new AI advances.
  
• Scientific rigor: methodical approach to experimentation and benchmarking.
  
• Tea