Senior Artificial Intelligence /lean Project Management Specialist

Boeing Boeing · Aerospace · Renton, WA +2

Boeing is seeking a Senior AI/Lean Project Management Specialist to lead AI strategy, workflow integration, and adoption within their Global Real Estate and Facilities organization. This role involves managing complex AI and data programs from initiation to closeout, ensuring alignment with enterprise AI strategy, and overseeing data governance, model validation, and compliance. The specialist will also drive continuous process improvement, manage risks, and coordinate with internal and external partners to deliver AI-enabled solutions that optimize business outcomes.

What you'd actually do

  1. Leads AI strategy, workflow integration, and adoption technology within GREF, advising leaders on how AI and existing enterprise tools can be leveraged to improve business outcomes and operational effectiveness
  2. Ensures GREF initiatives align with Enterprise Services and Boeing’s broader AI strategy, governance, and adoption objectives while identifying scalable opportunities to optimize workflows and ways of working
  3. Drives continuous process improvement and captures lessons learned specific to AI initiatives
  4. Leads all phases of highly complex AI and data programs from initiation through closeout, directing cross-functional teams across multiple organizations and geographies
  5. Coordinates data governance, model validation, testing, and compliance activities to ensure regulatory, security, and ethical AI requirements are met, while providing program governance including status briefings, executive reports, decision logs, model/version change control, and escalation

Skills

Required

  • Bachelor's degree or higher
  • 5+ years of experience developing and supporting project plans through the full project life cycle
  • 5+ years of experience in the implementation and use of Project Management Best Practices (PMBP) along with processes and methods
  • 5+ years of experience with AI/ML and generative AI lifecycle concepts, including model development, evaluation, deployment, monitoring, change management, documentation, and data governance
  • 5+ years of experience with RIO (Risks, Issues and Opportunities) processes and tools
  • 5+ years of experience leading cross-functional teams and working with senior leadership
  • 5+ years of experience presenting and communicating data in senior leadership briefings
  • 5+ years of experience working with Microsoft Office including Outlook, Excel, Word, PowerPoint, SharePoint, and Teams
  • 5+ years of experience using Microsoft Project

Nice to have

  • MBA or equivalent advanced degree
  • Project Management Professional (PMP) or equivalent certification
  • Experience with AI/ML concepts, MLOps, data engineering, model validation, and monitoring
  • Experience in real estate, facilities, asset-intensive or regulated environments
  • Experience with global programs and ability to lead initiatives across geographic locations
  • Experience in influencing and leading senior leaders at the executive level
  • Experience with Lean, Agile, DevOps or continuous improvement methodologies
  • Experience with cloud platforms (Azure, AWS, GCP), data governance frameworks, and AI ethical principles

What the JD emphasized

  • AI/ML model delivery
  • data platform deployments
  • AI strategy
  • AI adoption
  • AI and data programs
  • model validation
  • regulatory, security, and ethical AI requirements
  • AI delivery best practices
  • model lifecycle management
  • AI solutions
  • data quality
  • model drift
  • explainability
  • vendor risk
  • MLOps
  • model lifecycle management

Other signals

  • AI/ML model delivery
  • data platform deployments
  • automation and process optimization
  • organizational change for AI adoption
  • vendor/partner engagements
  • governance across asset-intensive environments
  • AI strategy, workflow integration, and adoption technology
  • continuous process improvement
  • AI and data programs from initiation through closeout
  • data governance, model validation, testing, and compliance activities
  • regulatory, security, and ethical AI requirements
  • AI delivery best practices
  • model lifecycle management
  • stakeholder requirements for AI solutions (data, performance, explainability, security)
  • Risks, Issues and Opportunities (RIO) specific to AI projects (data quality, model drift, explainability, vendor risk)
  • MLOps
  • model lifecycle management
  • AI-enabled solutions that drive measurable value