Senior AI-Native Backend Engineer

What You’ll Do

• Design and build backend services that power Goodwin’s charter aviation platform
• Architect scalable RESTful APIs used across the product
• Design and maintain PostgreSQL schemas that model complex trip, pricing, and payment workflows
• Build server-side applications using Node.js and Express
• Work with AWS services and DevOps tooling to deploy, monitor, and maintain production systems
• Optimize queries and data flows to support real-time operational decision making
• Own and scale infrastructure within AWS
• Collaborate with frontend engineers to connect user-facing workflows with backend systems
• Partner with product and operations teams to translate aviation workflows into software solutions
• Provide production support during critical operational periods when needed
• Improve system observability through logging, monitoring, and performance instrumentation

AI Expectations

• Use AI tools and frameworks such as LangGraph to build and improve real-world systems and workflows
• Experiment with new tools, frameworks, and approaches, and evaluate what is worth bringing into production
• Introduce new ideas, tools, or approaches that push beyond what the team is currently using
• Apply strong judgment to determine where AI meaningfully improves outcomes and where simpler solutions are more effective
• Share learnings and establish patterns that elevate how the broader team uses AI in day-to-day development

What Does Success Look Like in This Role?

• The platform reflects real-world aviation workflows in a way that feels fast, intuitive, and reliable for brokers and operators
• Backend systems handle complex sourcing, pricing, payments, and trip data seamlessly, even under high volume and real-time pressure
• New capabilities fit naturally into the platform because systems are designed with long-term scale and flexibility in mind
• You consistently ship high-quality features quickly and reliably, owning work end-to-end from idea through production
• You identify where AI can meaningfully improve how we build and operate, and implement those improvements in real workflows
• The team becomes more effective over time through the tools, patterns, and workflows you introduce
• When issues arise, they are resolved quickly because systems are observable, well-structured, and owned end-to-end