Neo Enterprise Engineering Manager

Capgemini is an AI-powered global business and technology transformation partner with nearly 60 years of heritage, employing over 420,000 team members in more than 50 countries. The Group reported 2025 global revenues of €22.5 billion.

• Bachelor's or Master's degree in Computer Science, Engineering, AI, Data Science or a related field
• Experience leading engineering teams in frontend, full-stack, or AI product environments
• Strong expertise in modern frontend technologies such as React, Angular, or Vue
• Solid understanding of backend development including APIs, JavaScript/TypeScript, Node.js, Express or NestJS
• Practical knowledge of AI/ML product concepts including LLMs, RAG, embeddings, vector search, and prompt engineering
• Experience working with Python in AI/LLM contexts including model integration, orchestration, data processing, evaluation
• Experience delivering enterprise-grade applications with focus on security, observability, and performance
• Familiarity with cloud platforms, preferably GCP (Cloud Run, BigQuery, Firestore, Cloud Storage, Vertex AI, Apigee)
• Strong collaboration and stakeholder management skills, working across Product, UX, Engineering, and Architecture in agile environments

Nice to have:

• Experience with GCP

• Lead and develop a team of frontend, full-stack, and AI product engineers to deliver high-quality product experiences
• Drive delivery of user-facing AI capabilities including chat, agents, knowledge retrieval, prompt flows, and feedback loops
• Ensure scalable, performant, and maintainable frontend architecture using modern frameworks (e.g. React)
• Guide teams on AI-ready implementation patterns such as RAG flows, prompt orchestration, streaming, and evaluation
• Collaborate closely with Product, UX/UI, AI/ML, Backend, Security, Cloud, and QA teams
• Translate business and product needs into clear engineering plans, milestones, and standards
• Establish best practices across code quality, CI/CD, testing, observability, accessibility, and performance
• Drive continuous improvement using user feedback, adoption data, and AI performance insights