Lead Generative AI Engineer

Il y a 2 mois

Paris, France AXA Group Operations Temps plein

About the job

You will be part of the Artificial Intelligence Engineering team, in the Group Emerging Technologies and Data (GETD) division of AXA. This transversal team’s mission is both to build AI-powered initiatives (proofs of concept, proofs of value, pilots) with AXA entities & strategic partners and to define & implement AI and GenAI best practices, tools, and collaboration models to be followed across the whole AXA Group. Our team is composed of 10 people, spread in 3 countries (France, Spain & Switzerland) and we work in hybrid mode (60% remote + 40% on-site).

AXA is a global leader in insurance and asset management present in nearly 60 countries. We leverage Artificial Intelligence to protect our 100+ million customers, in every domain of core insurance (Property & Casualty, Life & Savings, Health, …). As a responsible company, AXA defined and follows strong Responsible AI principles around robustness, interpretability, fairness, and sustainability.


Key responsibilities

As an LLMOps Engineer Expert, you will be responsible for providing expertise on overseeing the complete lifecycle management of large language models (LLM) across AXA Entities. This includes the development of strategies for deployment, continuous integration and delivery (CI/CD) processes, performance tuning, and ensuring high availability of our LLM services.

You will collaborate closely with data scientists, AI/ML engineers, and IT teams to define and align LLM operations with business goals, ensuring seamless and efficient operating model.

In this role, you will:

  • Define and disseminate LLMOps best practices to AXA entities.
  • Evaluate and compare different LLMOps tools to incorporate the best practices.
  • Stay updated on industry trends and advancements in LLM technologies and operational methodologies.
  • Participate into architecture design/validation sessions for the Generative AI use cases with entities.
  • Contribute to the development and expansion of GenAI use cases, including standard processes, framework, templates, libraries, and best practices around GenAI.
  • Design, implement, and oversee the infrastructure required for the efficient operation of large language models in collaboration with AXA entities.
  • Provide expertise and guidance to AXA entities in the development and scaling of GenAI use cases, including standard processes, framework, templates, libraries, and best practices around GenAI
  • Serve as the expert and representative on LLMops Practices, including: (1) Developing and maintaining CI/CD pipelines for LLM deployment and updates, (2) Monitoring LLM performance, identifying and resolving bottlenecks, and implementing optimizations, (3) Ensuring the security of LLM operations through comprehensive risk assessments and the implementation of robust security measures.
  • Collaborate with data and IT teams to facilitate data collection, preparation, and model training processes.


What is needed to succeed?

As we want you to succeed in this role, here is a list of examples of key factors:

  • Proven experience in the industrialization of GenAI projects by applying best LLMOps practices.
  • Strong understanding of computer science.
  • Familiarity with LLM models and associated techniques, as well as the tools and frameworks used to implement them (LangChain, LangSmith, Vector store, RLHF, …).
  • Proven Experience in LLM monitoring and observability.
  • Ability to communicate effectively with both technical and non-technical audiences.
  • Ability to lead technical presentations, demonstrations, workshops, architecture design sessions, proofs of concept, and pilots to explain.
  • Adaptability and willingness to learn new things and stay on top of the GenAI trends.
  • Strong humility and a collaborative approach to problem solving.
  • Strong relationship-building and influencing skills.
  • Resilience and the ability to thrive in a dynamic, fast-paced work environment.


Nice to have

Here are other elements we will consider:

  • Practical knowledge of Infrastructure as Code.
  • Insurance & Finance functional knowledge.

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