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. CogAE106 Show more Show less

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.

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. 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.