AI Engineer Expert

Drive innovation with cutting-edge AI & GenAI solutions that generate real business impact. Collaborate across teams, optimize AI performance, and shape responsible, scalable AI systems in a fast-paced, transformative environment. Join us to lead the future of AI engineering

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About The JobJob purpose

As an AI Engineer Expert, your main objective is to design, build, deploy, and continuously improve production-grade AI and GenAI solutions that support AXA Group’s strategic priorities. Working at the forefront of GenAI and Agentic AI technologies, you will help transform innovation into concrete business value across AXA entities.

This is a high-exposure opportunity to contribute to AXA’s AI transformation through visible, business-critical initiatives. You will work closely with AXA Group and entity stakeholders, bringing strong hands-on engineering expertise across topics such as GenAI evaluation, AI usage, and cost governance, while contributing to scalable, reliable, secure, and measurable AI capabilities.

Main missionsYour Responsibilities Include

• Work closely with AXA entities and business units to understand needs, clarify requirements, and contribute to AI solutions that are adopted and useful in real operational contexts.
• Design, develop, deploy, and operate AI and GenAI solutions that deliver measurable value for AXA entities.
• Contribute to the evolution of shared AI engineering practices, including evaluation, observability, performance, cost efficiency, safety, and lifecycle governance.
• Support key topics such as GenAI application evaluation and AI consumption optimisation, while maintaining flexibility to take ownership of new AI engineering priorities over time.
• Collaborate with cross-functional teams including data scientists, data engineers, product managers, platform teams, and business stakeholders to identify opportunities and translate them into robust technical solutions.
• Apply and contribute to reusable engineering patterns, standards, and guardrails for scalable, reliable, secure, and responsible AI systems across the organisation.
• Contribute to the broader AI engineering community by sharing practices, documenting learnings, and supporting collective improvement across teams.

Expected Skills & Experience

We are looking for someone with the following experience and skills:

Experience

• 3+ years delivering AI/ML-enabled products, GenAI applications, or agentic systems in production environments, with experience across the full lifecycle from design and implementation to monitoring and continuous improvement.
• Experience contributing to evaluation, observability, reliability, or optimisation practices for GenAI applications such as RAG, conversational assistants, workflow automation, or agentic systems.
• Good understanding of AI operational drivers, including quality, latency, cost, usage, safety, privacy, and business value, with the ability to balance them pragmatically.
• Experience contributing to technical delivery in cross-functional teams, with the ability to take ownership of work packages and collaborate effectively with peers.
• Experience working in the insurance, financial services, or another regulated industry is a plus.

Technical Skills

• Strong software engineering foundations for production-grade AI systems, including Python or similar languages, APIs, integration patterns, monitoring, and automation.
• Solid understanding of GenAI application patterns, including RAG, conversational assistants, workflow automation, and agentic or tool-using systems.
• Ability to define and operate evaluation approaches for GenAI systems, covering quality criteria, regression testing, human feedback, performance measurement, reliability, and risk controls.
• Understanding AI consumption and cost drivers, such as model selection, context management, prompt design, caching, routing, orchestration, infrastructure choices, and usage governance.
• Experience with observability and operational monitoring for AI applications, including tracing, latency, reliability, usage, cost efficiency, and continuous improvement indicators.
• Ability to establish practical guardrails for responsible AI engineering, balancing performance, cost, safety, security, privacy, sustainability, and compliance requirements.

Soft Skills / Transversal Skills

• Collaboration: ability to work effectively with engineers, data scientists, product teams, and business stakeholders in multicultural, cross-functional environments.
• Delivery mindset: organize and prioritize work, contribute to planning, manage dependencies, and help deliver GenAI initiatives with pragmatism and quality.
• Stakeholder interaction: translate business needs