Software Developer-builder (experienced or Senior)

Boeing Boeing · Aerospace · Seattle, WA +1

Software Developer-Builder role focused on modernizing systems and building new cloud-native applications for digital aircraft maintenance products, with a strong emphasis on integrating AI/ML/LLM capabilities like agentic workflows and RAG pipelines into production systems. The role involves full-stack development, CI/CD, and leveraging AI coding tools.

What you'd actually do

  1. Building full-stack, production-grade applications — frontend interfaces, backend services, REST and GraphQL APIs, and the integrations that tie them together in complex enterprise environments.
  2. Writing clean, testable, well-structured code across multiple languages and paradigms — and reviewing others' code with the same standard.
  3. Designing and implementing event-driven and microservices architectures that can scale, evolve, and be maintained by teams who didn't build them.
  4. Integrating AI, ML or LLM capabilities directly into client applications — building agentic workflows, RAG pipelines, AI-augmented developer tooling, and intelligent automation that actually works in production.
  5. Setting up and improving CI/CD pipelines, test automation, and delivery infrastructure so teams can ship with confidence.

Skills

Required

  • full-stack development
  • backend services
  • REST and GraphQL APIs
  • event-driven architectures
  • microservices architectures
  • CI/CD pipelines
  • test automation
  • cloud-native architecture
  • containerization
  • Kubernetes
  • infrastructure as code
  • observability
  • application development
  • software lifecycle
  • coding
  • testing
  • integrations
  • deployments
  • production support

Nice to have

  • AI coding tools like Claude, Cursor, or Windsurf
  • functional programming
  • object-oriented programming
  • AI-assisted development
  • multi-cloud setups
  • managed container services
  • algorithms
  • data-driven solutions
  • experimentation
  • monitoring
  • data pipelines

What the JD emphasized

  • built production applications from scratch and modernized legacy systems
  • delivered full-stack solutions
  • worked inside large enterprise environments with disparate/distributed systems and multi-cloud setups
  • built or been closely embedded with operations teams
  • built or integrated AI capabilities into real applications
  • know what it takes to get an AI-powered or ML/LLM feature into production and keep it working

Other signals

  • integrating AI/ML/LLM capabilities directly into client applications
  • building agentic workflows, RAG pipelines, AI-augmented developer tooling
  • getting an AI-powered or ML/LLM feature into production and keep it working