Greenfield Full Stack Developer

Boeing•Vancouver, BC

3d•CA$74,000 - CA$132,000•Hybrid

About The Position

Boeing Vancouver 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 Richmond BC, 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.
Must be legally able to work in Canada.
Individuals must not pose a risk for safeguarding of controlled goods.
Must be eligible to handle US export-controlled data.

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.