Key Responsibilities 

AI Trip Planning Tool – Ownership & Development 

• Own the full technical lifecycle of the B&R AI Trip Planning Tool: architecture, development, evaluation, iteration, and deployment. 
• Design and implement LLM-powered features that encode B&R’s institutional knowledge of trip planning, traveler segmentation, and destination expertise. 
• Build robust data pipelines to ingest, clean, and structure B&R’s fragmented trip data (itineraries, pricing, supplier info, historical bookings) into a unified, queryable foundation. 
• Develop and maintain integrations with B&R’s evolving systems landscape 
• Implement evaluation frameworks and feedback loops to continuously improve model accuracy, relevance, and business alignment. 
• Proactively identify and address data quality issues, scalability constraints, and emerging risks in the AI system. 

Data Architecture & Integration 

• Lead the data organization sprint required to make B&R’s existing data AI-ready by designing schemas, migrating content, and establishing data governance practices. 
• Architect and build APIs that expose AI capabilities to internal tools, the B&R website, and future client-facing surfaces. 
• Manage integrations with third-party AI providers, vector databases, and cloud infrastructure relevant to the AI stack. 
• Ensure data security, privacy compliance, and responsible AI practices across all system components. 

Executive Partnership & Stakeholder Collaboration 

• Serve as the primary day-to-day technology owner of AI project(s), reporting to the CEO or Director, Digital Experience, and providing clear, candid updates on progress, blockers, and trade-offs. 
• Translate high-level business goals—trip conversion, client satisfaction, planner efficiency—into well-scoped technical features and measurable outcomes. 
• Communicate technical complexity clearly to non-technical stakeholders, including trade-offs between build speed, cost, and quality. 

Organization-Wide Collaboration 

This role is a connective tissue position. The AI trip planning tool will only succeed if it reflects how B&R actually operates—and that requires deep, ongoing collaboration across every part of the business. You will be expected to build relationships across the organization and treat internal subject matter experts as co-authors of the product. 

Sales: Partner closely with the Sales team to understand how trip recommendations are built, how client preferences are captured, and where an AI layer can accelerate the journey from enquiry to booking. The tool must map to real sales workflows, not a simplified version of them. 

Operations & Trip Planning: Embed with the Operations team to learn the end-to-end logistics of how a B&R trip comes together (ie. supplier relationships, guide assignments, ground operations, contingency planning) and encode that knowledge into the AI system. 

Marketing: Collaborate with Marketing on how the AI tool surfaces to clients and supports campaign goals, conversion optimization, and brand storytelling across digital touchpoints. 

Finance & Pricing: Work with Finance to understand how trip pricing, margin structures, and packaging logic should inform recommendations and outputs. 

Leadership: Maintain a standing cadence with executive stakeholders to align on priorities, surface blockers, and report on tool performance and business impact. 

Whole organization: Proactively seek out institutional knowledge wherever it lives (ie. in long-tenured team members, historical trip files, supplier contacts, and operational playbooks) and treat that knowledge as the raw material the AI is built on. 

 Engineering Standards & Delivery 

• Lead code reviews, uphold development standards, and champion engineering best practices. 
• Contribute to the broader Digital Experience team’s technical roadmap from an AI and data platform perspective. 
• Actively participate in Agile ceremonies and collaborate with the Agile Project Coordinator on scoping and delivery planning. 
• Where applicable, mentor junior developers on AI tooling, prompt engineering, and data pipeline best practices.