AI Value Architect

Job Summary :

The AI Value Architect owns the AI value journey for a business area (for example Revenue and Commercial, Operations and Guest, or Finance, HR and Cargo) end to end: all the AI we build and run to create value there. You are an elevated technical product leader who sits between the business and the engineers: you turn business needs into a prioritized backlog, lead a standing squad, and still build alongside them. This is a leadership role, not a facilitation one. You are the AI counterpart to the business area’s leadership, and you align AI efforts across squads so the whole vertical compounds rather than fragments.

Accountabilities & Responsibilities

• Own the value, not just the delivery. Start from the business outcome and the why, then lead your squad from discovery to production and measurable impact.
• Own the AI value journey for your business area single-threaded: one owner, one set of outcomes, and clear accountability to the business.
• Translate business needs into a prioritized AI backlog with your Business Product Owner, and make the calls on what to build, what to defer, and what not to build.
• Lead a standing squad of Forward Deployed AI Engineers, set the technical direction, and keep quality, evaluation, security and cost on track.
• Stay hands-on: code alongside the team, review, and shape the agentic architecture. No overhead, everyone builds, the AI Value Architect included.
• Define the success measures, feedback loops and validation gates for your business area’s AI work, and build the measurement discipline to show the business impact of the AI investment.
• Run tight feedback loops and take a systems view across the delivery lifecycle: know when to adjust, redesign or pivot, and balance your squad’s capacity between building reusable AI capability and delivering with it.
• Align AI efforts across business areas and squads: find synergies, reuse patterns, surface and unlock underused capability, and prevent parallel reinvention, on the Hub’s paved road and MCP fabric.
• Be the AI counterpart to your business area’s leadership: build trust, manage expectations on cost, performance and feasibility, and turn AI into outcomes the business owns.
• Coach and grow your squad and business partners on effective and responsible AI use, lift the business area’s AI fluency over time, and build lasting AI expertise in a standing team rather than project-hopping.
• Protect the guest and the operation: keep solutions human-centric and speak up when AI is not the right tool.

Education & Experience

• We look for a technical product leader who pairs deep business understanding with hands-on agentic-AI engineering and owns outcomes end to end:
• Deep business understanding: you sit with senior stakeholders as a credible peer and turn business goals into AI outcomes, ideally in aviation or a comparable operations-heavy domain.
• Hands-on engineering: you have built and shipped agentic AI and production software yourself, and you still code alongside your squad. This is a build-first leader, not a manager who has left the tools behind. You keep your own AI
• fluency current and stay close to new AI capabilities relevant to your business area, so your advice carries weight.
• Single-threaded ownership: a track record of owning a product or an end-to-end outcome from discovery through production to measurable business impact.
• Experience leading, coaching and growing a small technical team, and setting direction on architecture, quality, evaluation, security and cost.
• Strong grasp of the agentic AI stack the squads run on: LLM orchestration, tool calling, RAG, MCP integrations, guardrails, evaluation, and the trade-offs of latency, quality, cost and reliability.
• A record of aligning work across teams, defining success measures and feedback loops, and turning scattered efforts into reusable patterns and shared platforms.
• Curiosity and a business-first, human-centric mindset: aviation is made for humans, by humans, and AI supports people, it does not replace them. Fluent English, comfortable in a culturally diverse, international team.
• Typically 8+ years across software and AI, including hands-on GenAI and LLM work and time owning delivery. A natural next step for a Forward Deployed AI Engineer (Technical Lead) who has grown into business ownership.
• Master’s degree, or a strong Bachelor’s degree, in Computer Science, Software Engineering, Data Science, AI/ML or a related technical field, or equivalent practical experience; relevant cloud-AI, GenAI, agentic-AI or MLOps certifications are an advantage.

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