Greenfield Full Stack Developer

Boeing•Tukwila, WA

5d•Hybrid

About The Position

Boeing Digital Services is seeking a Greenfield Full Stack Developer to support end-to-end software delivery within our digital aircraft maintenance product suite. This role is grounded in strong software development fundamentals, with an emphasis on leveraging modern AI-assisted tools to build cloud-native systems at the cutting edge of aerospace technology. You will work closely with Developers, Engineering, Greenfield Leadership, Data Science, and Architects to deliver product features and capabilities that keep AI and ML at the center of our solutions. This is a hybrid position based out of Denver, CO or Tukwila, WA , requiring 2–3 days per week on-site.

Requirements

2 or more years' professional experience in an application development role, performing core activities across the software lifecycle — from requirements gathering and design through coding, testing, integrations, deployments, and production support
2+ years of proven experience with full-stack web development
2+ years of experience with modern software product development, integration, and delivery
2+ years of hands-on development experience in a collaborative team environment using Agile principles, structured code review, and automated DevOps processes and tooling
Experience working with relational or NoSQL databases (e.g., PostgreSQL, Azure SQL, MongoDB, or equivalent) — including data modeling, querying, and integration across application layers
built production applications from scratch and modernized legacy systems — comfortable with both greenfield and brownfield approaches, and know when to apply patterns like strangler fig or lift-and-refactor
fluent in multiple programming languages and paradigms, writing clean and testable code in both functional and object-oriented styles, knowing how to pick the right tool for the job
delivered full-stack solutions — frontend web apps, backend APIs, cloud-hosted services, and standalone applications — and understand how those pieces fit together end-to-end in complex enterprise environments
worked inside large enterprise environments with disparate and distributed systems — you know how to navigate organizational complexity and deliver in spite of it
comfortable owning your segment of the software development lifecycle end-to-end: writing code, reviewing pull requests, writing tests, and collaborating effectively with developers on both sides of you
understand cloud-native architecture — containerization, microservices, event-driven patterns — and are comfortable with source control branching strategies and CI/CD automation
built or been closely embedded with operations teams, and understand cloud-native platform adoption (Kubernetes, managed container services), infrastructure as code, observability, and what it means to build software that can be run in production

Nice To Haves

Bachelor's degree in Computer Science, Software Engineering, Electrical Engineering, Information Systems, or a related technical field
Advanced degree (Master's or equivalent) in Computer Science, Software Engineering, Data Science, Artificial Intelligence, Machine Learning, or a related discipline — particularly for candidates with experience in applied algorithms, AI/ML, or advanced system design
Experience or exposure integrating AI, ML, or LLM capabilities into software applications, including model APIs, prompt orchestration, retrieval-augmented generation, or intelligent automation workflows
Experience with cloud-native architecture and delivery tooling on Microsoft Azure (e.g., AKS, Azure Functions, Azure DevOps) or equivalent platforms — including containerization, infrastructure as code, and observability
Experience modernizing legacy systems or working in brownfield environments, including approaches such as strangler fig, lift-and-refactor, or incremental modernization
Experience working with structured and unstructured data, including data cleaning, feature design, validation, quality checks, or analytical workflows
Familiarity with Databricks or similar unified data and AI platforms — a strong plus for candidates supporting data pipeline or ML-integrated feature work
Demonstrated ability to collaborate effectively across cross-functional teams, partnering with Product, Design, Engineering, Data Science, Architecture, or Operations to deliver business outcomes
Experience in aerospace, industrial, manufacturing, transportation, or similarly complex regulated US-based environments

Responsibilities

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
Writing clean, testable, well-structured code across multiple languages and paradigms, and reviewing others' code to the same standard
Writing and integrating tests (unit, integration, system) across the development process to ensure quality from local development through to deployment
Designing and implementing event-driven and microservices architectures that can scale, evolve, and be maintained by teams who did not build them
Integrating AI, ML, or LLM capabilities directly into client applications — including agentic workflows, RAG pipelines, AI-augmented developer tooling, and intelligent automation that works reliably in production
Setting up and improving CI/CD pipelines, test automation, and delivery infrastructure so teams can ship with confidence
Mentoring and uplifting developers around you through pairing, code review, and real-time feedback that raises the performance of the whole team
Keeping product leadership and architects informed of technical risk, blockers, and changes that affect delivery
Apply algorithms and data-driven approaches to solve complex web and application challenges
Translate business needs into clear technical requirements, implementation approaches, and measurable success criteria
Work with application data across validation, transformation, and integration touchpoints spanning front-end and back-end systems
Evaluate solution performance using testing frameworks, production metrics, and user feedback loops
Support experimentation, monitoring, and data pipelines that ensure reliable application behavior in production
Build or integrate AI capabilities into real applications — with demonstrated understanding of what it takes to get an AI-powered or ML/LLM feature into production and keep it working
Use AI-assisted development tools (e.g., Codex) as a natural part of your daily workflow to move faster and produce higher-quality output