AI Product Delivery Lead

Spirit AeroSystems•Wichita, KS

17h

About The Position

This role is at Spirit AeroSystems, Inc. a wholly owned subsidiary of The Boeing Company, supporting Spirit’s Commercial Business Units (“Spirit Commercial”). Spirit Commercial designs and builds commercial aerostructures, including for Boeing Commercial Airplanes, one of Boeing’s three business units and the premier manufacturer of commercial jetliners for decades. Spirit Commercial’s core products include fuselages, pylons, nacelles and wing components, with a focus on innovative composite and aluminium manufacturing solutions.

Requirements

Bachelor's Degree in Statistics, Mathematics, Computer Science, or another related field of study
6-9 years’ related experience with a Bachelor’s, 4-7 years’ related experience with a Masters
Experience leading delivery of complex technology, data, analytics, or AI initiatives from requirements through production deployment
Demonstrated experience delivering AI or ML‑enabled products or programs in a cross‑functional environment
Proven ability to manage scope, dependencies, risks, budgets, and executive expectations across multiple concurrent initiatives
Experience with cloud‑based data and AI platforms (e.g., Azure, AWS)
Strong ability to build credibility and communicate effectively with technical and non‑technical stakeholders
Comfortable operating in ambiguity and capable of moving work forward without complete information
Clear, concise communicator in written and verbal settings

Nice To Haves

Experience in Manufacturing
Familiarity with the AI/ML development lifecycle, experimentation workflows, and model deployment patterns
Exposure to Responsible AI principles, including fairness, explainability, risk mitigation, and monitoring for drift

Responsibilities

Own end‑to‑end delivery of AI initiatives, from early scoping and prioritization through production launch and post‑launch evaluation.
Establish delivery standards, operating rhythms, and performance measures that improve predictability and reduce project risk.
Partner with enterprise architecture, engineering, and security to drive architectural decisions and define practical, scalable implementation approaches for AI solutions.
Lead cross‑functional alignment with business stakeholders, data science, engineering, infrastructure, and security; translate requirements across teams without loss of clarity or intent.
Conduct discovery with business partners to assess feasibility, define priorities, establish measurable success criteria, and set clear expectations before development begins.
Assess when AI is and isn’t the right solution and communicate rationale effectively across all stakeholder levels.
Build and maintain delivery plans that account for data readiness, model performance constraints, integration complexity, and other sources of AI‑specific uncertainty.
Surface risks early, manage trade‑offs, and drive decisions when technical realities and business needs are misaligned.
Monitor post‑launch outcomes, validate that solutions deliver intended value, and guide iteration where needed.
Lead and develop AI delivery resources, providing direction, coaching, and accountability to ensure high‑quality execution.