Senior AI Engineer / Senior Data Scientist, Descartes & Mauss

• 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)
• 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

Desirable

• 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

• Initial HR Call
• Case Study
• Technical debrief with Hiring Managers
• Office tour
• Decision