Experienced or Senior Greenfield Software Developer

Boeing•Richmond, BC

1d•CA$94,000 - CA$197,000•Hybrid

About The Position

Boeing Vancouver is seeking an experienced or senior level Software Developer (Builder) who will help support the end-to-end software delivery. This role is a hybrid position (2-3 days per week) and can be worked out of our Richmond, BC office. This is first and foremost grounded in Software Developers who bring both passion and experience in leveraging the latest AI tools to build cloud-native systems for cutting edge digital aircraft maintenance products. You will work closely with other Developers (Builders), Engineering, Greenfield Leadership, Data Science, and Architects to deliver product features and capabilities that keep AI/ML at the center.

Requirements

3+ years of professional software development / engineering experience delivering production software in enterprise, cloud, or product development environments.
3+ years proven full-stack development experience building and shipping applications that include frontend user interfaces, backend services, APIs, and system integrations.
3+ years of experience with modern software delivery practices, including source control, branching strategies, automated testing, CI/CD pipelines, and release management.
3+ years of hands-on experience writing clean, testable, production-quality code and participating in the full SDLC, including code review, debugging, testing, and deployment support.
Must be legally authorized to work in Canada.
Must not pose a risk to the safeguarding of controlled goods.
Must be eligible to handle U.S. export-controlled data.

Nice To Haves

5+ years of professional software development / engineering experience delivering production software in enterprise, cloud, or product development environments.
5+ years proven full-stack development experience building and shipping applications that include frontend user interfaces, backend services, APIs, and system integrations.
5+ years of experience with modern software delivery practices, including source control, branching strategies, automated testing, CI/CD pipelines, and release management.
5+ years of hands-on experience writing clean, testable, production-quality code and participating in the full SDLC, including code review, debugging, testing, and deployment support.
Experience or exposure integrating AI, ML, or LLM capabilities into software applications, including working with model APIs, prompt orchestration, retrieval-augmented generation, or intelligent automation workflows.
Experience or exposure building or integrating AI/ML/LLM-powered product features in production.
Experience with cloud-native architecture and delivery tooling, such as containerization, Kubernetes or managed container services, infrastructure as code, and observability platforms.
Experience modernizing legacy systems or working in brownfield environments, including approaches such as strangler fig, lift-and-refactor, or incremental modernization.
Experience working in complex enterprise environments with distributed systems.
Experience designing or contributing to event-driven or microservices-based systems that are scalable, maintainable, and production-ready.
Experience working with structured and unstructured data, including data cleaning, feature design, validation, quality checks, or analytical workflows.
Demonstrated ability to collaborate effectively in a cross-functional team, partnering with Product, Design, Engineering, Data Science, Architecture, or Operations to deliver business outcomes.
Experience in aerospace, industrial, manufacturing, transportation, or similarly complex regulated 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 with the same standard.
Write tests (unit, integration, system etc) and integrate them across the development process.
Ensuring quality from local development to deployment.
Designing and implementing event-driven and microservices architectures that can scale, evolve, and be maintained by teams who didn't build them.
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.
Setting up and improving CI/CD pipelines, test automation, and delivery infrastructure so teams can ship with confidence.
Mentoring and uplifting the developers around you — pairing, code review, and real-time feedback that makes the whole team better.
Keeping product leadership and architects informed of technical risk, blockers, and changes that affect the engagement.