Senior Artificial Intelligence (ai) Solutions Architect

Boeing Boeing · Aerospace · Seattle, WA +1

Senior AI Solutions Architect at Boeing, focusing on designing and leading end-to-end AI-enabled solutions for the Supply Chain organization. This role involves translating business needs into technical architectures for AI capabilities like ML, generative AI, and Agentic AI, covering the full lifecycle from data to visualization, and ensuring solutions are scalable, secure, and aligned with enterprise standards. The role also provides architectural guidance for AI product teams and supports production readiness.

What you'd actually do

  1. Lead the architecture and design of end-to-end AI solutions supporting BGS Supply Chain business needs
  2. Define solution architectures that integrate on-prem and cloud-based data, analytics, and AI capabilities into scalable enterprise patterns
  3. Translate business requirements into technical designs for AI-enabled supply chain solutions
  4. Architect the full solution lifecycle from raw data and governed data products through data modeling, ontology, AI model development, orchestration, and visualization
  5. Design solutions using ML, Generative AI, Agentic AI, deep learning, optimization, and other advanced AI techniques as appropriate to the use case

Skills

Required

  • Artificial Intelligence (AI) and Machine Learning (ML) technologies
  • Information Technology architecture
  • cloud and data architecture
  • large-scale, hybrid cloud and on-prem environment
  • communicating with technical experts and explaining difficult technical concepts to non-technical business users
  • working across organizations and interfacing with key stakeholders, including senior leaders
  • designing, developing & optimizing AI/ML solutions, data science workflows, and analytics methodologies
  • AI/ML and generative AI lifecycle concepts
  • model development, evaluation, deployment, monitoring, change management, documentation, and data governance

Nice to have

  • Bachelor’s degree or higher in computer science, engineering, information systems, data science, or related field
  • architecting AI solutions for supply chain, logistics, inventory, planning, sourcing, or parts domains
  • ML platforms
  • Machine Leaning Operations (MLOps)
  • Large Language Model (LLM) orchestration
  • vector databases
  • retrieval-augmented generation
  • model lifecycle management
  • designing semantic layers, ontologies, knowledge graphs, or enterprise data models
  • Agentic AI, workflow automation, or AI-based decision support solutions
  • data visualization tools
  • orchestration platforms
  • front-end delivery patterns
  • enterprise data governance, cataloging, lineage, and security frameworks
  • event-driven architecture, API-based integration, and scalable cloud-native design
  • data architecture, APIs, integration patterns, and production engineering concepts
  • supporting industrial, aerospace, or similarly complex operational environments

What the JD emphasized

  • 10+ years of experience with Artificial Intelligence (AI) and Machine Learning (ML) technologies
  • 10+ years of experience with Information Technology architecture including cloud and data architecture in a large-scale, hybrid cloud and on-prem environment
  • 5+ years of experience designing, developing & optimizing AI/ML solutions, data science workflows, and analytics methodologies
  • 5+ years of experience with AI/ML and generative AI lifecycle concepts, including model development, evaluation, deployment, monitoring, change management, documentation, and data governance

Other signals

  • designing scalable, secure, and production-ready Artificial Intelligence (AI) solutions
  • translate raw data and data products into AI capabilities
  • architect the full solution lifecycle from raw data and governed data products through data modeling, ontology, AI model development, orchestration, and visualization
  • Partner with supply chain business stakeholders to identify high-value use cases and shape feasible technical solutions
  • Ensure solutions are scalable, maintainable, secure, and aligned to Boeing architecture, cyber, governance, and compliance standards
  • Provide architectural guidance for AI product teams throughout design, build, testing, deployment, monitoring, and enhancement phases
  • Support production readiness, performance tuning, observability, and operational support for deployed AI solutions