AI Engineer – Category Strategy Optimization Platform, FinTech

Role Description

About Us:

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.

We are seeking an AI Engineer to join the team, responsible for co‑developing and technically steering an advanced AI‑driven platform designed to enable data‑driven procurement decision‑making. The solution will be developed in collaboration with a third‑party technology partner, while  retains ownership of the technical direction, integration architecture, and data governance.

You will act as the internal technical lead, ensuring smooth integration of the vendor’s AI solution with ’s procurement ecosystem (Ivalua, Conga, ORO, …).

Your focus will be on defining system architecture, data models, and API integrations, validating vendor deliverables, and implementing Booking‑specific extensions. This is a hands‑on, technically oriented role that combines AI engineering expertise with the ability to oversee external development work.

Due to the rapid and continuous growth of the organization, the tasks and duties of this role may evolve over time. You will be keen to develop and grow within the role and to take on additional challenges and responsibilities as they occur.

• Act as the internal technical lead for the AI‑based Category Strategy Optimizer platform, developed in collaboration with a third‑party vendor.

• Validate the overall architecture, ensuring alignment between ’s systems (e.g., Ivalua, Conga, ORO) and the vendor’s solution components.

• Oversee and technically steer the vendor’s development activities, ensuring code quality, compliance, and performance standards are met.

• Collaborate with Procurement, Legal, Finance, and Technology teams to translate business and functional requirements into scalable technical solutions and ensure ongoing compliance with internal security, privacy, and AI governance frameworks.

• Review and validate vendor deliverables, including data models, APIs, and AI‑driven modules, ensuring adherence to ’s security and architecture standards.

• Develop integration scripts, validation routines, and Booking‑specific extensions to enhance functionality and ensure end‑to‑end system coherence.

• Partner with data and platform engineering teams to guarantee robust data flows, pipelines, and governance.

• Support implementation of CI/CD automation, monitoring, and model deployment in cloud environments (AWS / GCP).

• Provide technical documentation and training materials, supporting operational readiness and knowledge transfer.

• Identify opportunities for optimization, performance improvement, and functional enhancement in collaboration with the vendor and internal stakeholders.

Technical Requirements:

• Experience:
  
  4‑8 years of professional experience in AI engineering, MLOps, and AI application development.

• AI Application Development:
  Proven experience building and deploying AI applications—from prototype to production—using containerization (e.g., Docker).

• Programming
  
  Skills:
  
  Fluent in Python and SQL, with working knowledge of Type Script or JavaScript.

• Generative AI Expertise:
  Strong understanding of AI agent architectures, LLM fine‑tuning, prompt engineering, function calling, Retrieval‑Augmented Generation (RAG), and LLM orchestration.

• Agentic Frameworks:
  Hands‑on experience with Lang Chain, and familiarity with advanced agentic orchestration frameworks such as Lang Graph and CrewAI.

• Software & Deployment:
  Experience with CI/CD pipelines, model versioning, and scalable deployment of AI services.

• Cloud Environments:
  Proficiency in working with cloud platforms (AWS, Azure, or GCP) and integrating GenAI applications with enterprise systems.

• Statistical and Data Modeling:
  Solid understanding of regression, clustering, NLP, and generative modeling (LLMs, transformers).

• Data Management:
  Skilled…