Enterprise AI Engineer II

At , data drives our decisions. Technology is at our core. And innovation is everywhere. But our company is more than datasets, lines of code or A/B tests. We’re the thrill of the first night in a new place. The excitement of the next morning. The friends you encounter. The journeys you take. The sights you see. And the memories you make.

Through our products, partners and people, we make it easier for everyone to experience the world.

Enterprise Engineers in the Collaboration & Productivity track are the digital workplace enablers for all employees at  Think of us as the productivity accelerators of our organization, clearing the path so thousands of colleagues can do their best work, every day.

We are looking for an Enterprise AI Engineer II for our Central Tech Business Unit in the The Business Applications team which is part of the Productivity & Collaboration Track within the IT Services organization part of Central Tech.

This is role is to be based out of Amsterdam, Netherlands

The purpose of this role is to enhance and maintain AI Solutions in enterprise applications that support 's operations, contributing to the overall efficiency and productivity of the company. The Enterprise Application Engineer II owns solutions end-to-end, contributing greatly to the smooth operation of the enterprise. They support small, medium and large-scale product implementations to help the day-to-day work of colleagues, applying defined best practices and delivering value primarily within their team’s scope.

• Business Consultancy: Advise on business challenges and use AI tools to automate workflows and support data-driven decisions.

• Stakeholder Management, Collaboration, Communication: Effectively engaging with team members, product managers, and relevant stakeholders within the team scope, working collaboratively and clearly conveying information to achieve shared goals. Sharing knowledge and work on AI methodologies to develop the team.

• Analysis, Innovation, Problem-Solving: Examining information to identify issues and developing solutions using logical and analytical thinking. Challenges the status quo and, with the support of more senior engineers, delivers improved engineering practices. Actively identifies and applies AI and automation opportunities to streamline workflows and increase team efficiency.

• Coaching, Knowledge Sharing & Mentoring: Actively sharing experiences, knowledge, and work methodologies to facilitate growth and development of colleagues. Demonstrating awareness of mentoring and coaching practices, supporting more junior team members with their craft.

• Systems Design: Evaluates possible architecture solutions taking into account cost, business and technology requirements, and emerging AI-native technologies. Describes system implications and is able to design and defend solutions that meet current requirements and are adaptable for future enhancements, considering Engineering for Failure, Scaling, Data Storage and Caching, and Security and Compliance Standards. Evaluates and improves system health by applying engineering best practices.

• Technical Incident Management: Addresses and resolves live production issues by mitigating customer impact within SLA, taking the lead on production incidents. Tracks incidents by contributing to postmortem processes and root cause analysis, applying Incident Communication, Log Analysis and Incident Response Process practices. Demonstrates ability to triage, prioritize, troubleshoot, and monitor system performance using observability frameworks.

• Production systems best practices: Secures production systems by following company policies, standards, and industry security best practices. Ensures compliance (SOx, PCI, PII, GDPR) at all times. Reduces single points of failure by documenting system architecture and writing runbooks. Ensures resiliency and uptime by automating infrastructure provisioning, with sufficient knowledge of backup/restore procedures and awareness of Configuration Management and Data Retention strategies.

• AI Skills & Tooling (Required):
  Practical experience with AI technologies in a professional context, including LLM-powered applications, AI coding assistants (e.g. Git Hub Copilot, Codex, Claude Code, Windsurf, Cursor, etc), AI-based search and knowledge tools (e.g. Glean), prompt/context engineering fundamentals, and the ability to evaluate,…