Job Description

As a Lead AI Engineer, you will lead Generative AI missions for our clients end-to-end — from proof of concept to production — acting as the technical reference on autonomous agents, LLM deployment, and cost-efficient GenAI architectures.

Here's what that looks like in practice: an empty repo, a client's business need, and a couple of weeks to prove the idea holds. You build the POC yourself, the client is convinced — now hundreds of their employees want it on their desk every morning. You add single sign-on and roles, quotas so nobody drains the budget in an afternoon, an evaluation harness that flags quality drift, and pipelines that ship new versions without waking anyone up. Same person, same architecture, from first prototype to the service the client relies on.

Your Key Responsibilities

• AI Engineering & Agentic Workflows: design and lead the implementation of autonomous AI agents, multi-agent orchestration (LangGraph, CrewAI), and RAG pipelines with complex API integrations, on client missions;
• Hybrid LLM Deployment: define deployment strategies across managed cloud AI services (Azure OpenAI, AWS Bedrock, GCP Vertex AI) and open-weight models (Llama, Mistral) served on GPU infrastructure via vLLM or TGI;
• LLMOps, Monitoring & Evaluation: design observability for GenAI applications — quality, latency, hallucination detection — using tools such as LangSmith, Arize, or Prometheus/Grafana;
• AI FinOps: control cost across client AI projects through token management, prompt routing, caching, and GPU resource optimization;
• Technical Leadership: standardize containerization (Docker, Kubernetes) with client teams, mentor delivery engineers, and communicate the technical vision to client stakeholders.
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Qualifications

What we're looking for

Must-have:

• 3 years of overall professional experience, including hands-on delivery of GenAI/LLM projects in production;
• Deep expertise building agentic workflows and RAG pipelines (LangChain, LlamaIndex, LangGraph, or equivalent);
• Strong practical experience deploying and serving open-source models (Llama, Mistral) on GPU-accelerated infrastructure;
• Solid understanding of LLMOps practices, model evaluation, and GenAI monitoring tools;
• Good working knowledge of vector databases (Qdrant, Pinecone, Milvus, or equivalent) and Docker/Kubernetes;
• Comfortable framing client needs directly and leading technical design on client-facing missions;
• Fluency in both French and English is mandatory.
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Softskills: Advisory Mindset | Consultative Communication | Curiosity | Rigor | Business-Oriented

Nice-to-have:

• Prior exposure to modern data platforms, particularly Databricks, and general data engineering pipelines;
• Practical knowledge of DevOps/CI-CD methodologies applied to ML (MLOps);
• Experience with native AI/ML services across major cloud platforms (Azure, GCP, AWS);
• Cloud AI certifications (e.g., Azure AI Engineer Associate, AWS Certified Machine Learning – Specialty, GCP Professional ML Engineer);
• A track record of contributing to open-source AI projects or leading technical communities.