AI Engineer Lead

Lead AI innovation by designing scalable, secure GenAI solutions. Drive transformation, mentor teams, and collaborate with stakeholders to deliver measurable business value. Shape the future of AI at a global scale—where technology meets strategic impact.

About The Job

Job purpose

As an AI Engineering Lead, your main objective is to lead the design, delivery, and continuous improvement of 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 strategic business value across AXA entities.

This is a unique opportunity to grow at the heart of AXA’s AI transformation, contributing to high-visibility initiatives and engaging directly with AXA Group and entity executive leaders. You will provide technical leadership across a broad portfolio of AI engineering topics, including GenAI evaluation and AI usage and cost governance, while working closely with business and technology stakeholders to deliver scalable, reliable, secure, and measurable AI capabilities.

Main missions

Your Responsibilities Include

• Engage proactively with AXA entities and business units, facilitating ongoing dialogue, co-constructing AI solutions, and guiding stakeholders through complex decisions to ensure alignment, adoption, and shared success.
• Lead the design, development, deployment, and operation of 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.
• Define reusable patterns, standards, and guardrails for scalable, reliable, secure, and responsible AI systems across the organisation.
• Lead and mentor AI engineers, fostering engineering excellence, pragmatism, curiosity, and continuous improvement.

Expected Skills & Experience

Experience

• 5+ years delivering AI/ML-enabled products, GenAI applications, or AI platforms 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.
• Demonstrated leadership experience in managing engineering teams or cross-functional project teams.
• 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

• Team management: experience in leading, coaching junior team members
• Project management: lead end-to-end GenAI initiatives, define scope, milestones, timelines, budgets, resource planning, risk management, and ensure on-time, on-budget delivery.
• Stakeholder management: partner with product, business units, compliance, security, legal, and operations; translate business needs into concrete requirements; manage expectations; maintain