Principal Developer-builder

Boeing Boeing · Aerospace · Seattle, WA +1

Boeing is seeking a Principal Developer-Builder to own the end-to-end delivery of product capabilities, including designing solutions and leading a small team. This hybrid role involves building full-stack, cloud-native applications, integrating AI/ML/LLM capabilities, and implementing CI/CD pipelines. The role requires modern software development expertise, experience with production ML systems, and shipping AI-powered features. Responsibilities include technical delivery, team guidance, and upwards communication.

What you'd actually do

  1. Own the technical scope for your product capability, shaping the architecture, driving implementation, and planning delivery with your team.
  2. Building full-stack, production-grade, cloud-native applications: frontend interfaces, backend services, APIs, and the integrations that connect them across complex enterprise environments.
  3. Integrating AI, ML or LLM capabilities into your product.
  4. Serve as Builder Lead – guiding, coaching, instructing a small team of developers (ex: organizing work, breaking down tasks, pairing on hard problems, and keeping the team moving).
  5. Serve as a technical point of contact for your product capability — representing the work clearly to your product, design, and technical leads.

Skills

Required

  • Modern Software Development Expertise
  • Experience building and shipping full-stack applications in production.
  • Depth in at least one part of the stack and enough breadth to work across frontend, backend, APIs, and cloud-hosted services.
  • Proficiency in multiple programming languages and frameworks.
  • Ability to write clean, testable, production-quality code.
  • Experience making architecture decisions under technical, organizational, and delivery constraints.
  • Ability to document tradeoffs, explain decisions clearly, and remain accountable for production outcomes.
  • Experience working in enterprise environments with distributed/disparate systems, complex integrations, and multi-cloud or hybrid cloud setups.
  • Ability to own code quality for a team through implementation, code review, and technical coaching.
  • Working knowledge of cloud-native architecture, branching strategies, CI/CD automation, and application delivery practices.
  • Experience working with Operations, Platform, or Infrastructure teams.
  • Familiarity with managed container services, infrastructure as code, observability, and production support practices.
  • Experience architecting production ML systems that deploy, scale, and integrate models developed by Data Scientists.
  • Strong understanding of MLOps practices, including model versioning, CI/CD, testing, monitoring, and rollback.
  • Experience designing model-serving infrastructure for batch, real-time, and streaming use cases.
  • Ability to define scalable end-to-end ML architectures across data, features, models, and downstream applications.
  • Experience establishing operational standards for observability, reliability, governance, and performance of models in production.
  • Experience shipping AI-powered features to production.
  • Understanding of the lifecycle for AI-enabled features, including integration, evaluation, monitoring, and maintenance.
  • Regular use of AI coding tools such as Claude, Cursor, or Windsurf in day-to-day development work.
  • Experience with retrieval-augmented generation, LLM orchestration, or agent workflows, either hands-on or in a technical leadership role.
  • 7+ years of professional experience in an application development role, performing core activities across the software lifecycle- from requirements gathering and design to coding, testing, integrations, deployments, and production support
  • 7+ years proven experience with full-stack web development
  • 7+ years of experience with modern software product development, integration, and de

Nice to have

  • Guide and drive adoption of AI assisted development methods (such as agentic coding or SDD).
  • Uplift developers through code review, pairing, and day-to-day technical mentorship.

What the JD emphasized

  • Experience architecting production ML systems that deploy, scale, and integrate models developed by Data Scientists.
  • Strong understanding of MLOps practices, including model versioning, CI/CD, testing, monitoring, and rollback.
  • Experience designing model-serving infrastructure for batch, real-time, and streaming use cases.
  • Ability to define scalable end-to-end ML architectures across data, features, models, and downstream applications.
  • Experience establishing operational standards for observability, reliability, governance, and performance of models in production.
  • Experience shipping AI-powered features to production.
  • Understanding of the lifecycle for AI-enabled features, including integration, evaluation, monitoring, and maintenance.
  • Experience with retrieval-augmented generation, LLM orchestration, or agent workflows, either hands-on or in a technical leadership role.

Other signals

  • Integrate AI, ML or LLM capabilities into your product.
  • Experience shipping AI-powered features to production.
  • Experience with retrieval-augmented generation, LLM orchestration, or agent workflows.