About the role
What You’ll Do
• Build full-stack business applications across front-end, back-end, APIs, databases, integrations, authentication, and deployment.
• Build reliable AI workflows using model APIs, business data, structured outputs, retrieval, tool use, context management, and human review to complete multi-step tasks and appropriately route exceptions.
• Develop AI-powered business applications that combine model reasoning with structured data, business rules, human approvals, and safeguards.
• Build automations, integrations, and internal tools across CRM, finance, operations, communications, and other core business systems.
• Create secure integrations using REST APIs, webhooks, authentication, middleware, databases, and background automation.
• Test AI behavior and improve reliability through clear guardrails, permissions, monitoring, fallbacks, and error handling.
• Troubleshoot AI failures, data-sync issues, bugs, and performance problems.
• Take work from requirements through development, testing, deployment, documentation, and ongoing improvement.
What We’re Looking For
Five or more years of professional software development experience is preferred, but demonstrated technical ability, strong engineering fundamentals, and a meaningful body of work matter more than tenure.
• Strong full-stack development experience, with demonstrated ability to independently build, debug, deploy, and maintain production business applications across modern front-end and back-end technologies.
• Demonstrated ability to use modern AI development tools to improve how software is built, while maintaining the judgment, depth, and ownership expected of a strong developer.
• Genuine curiosity about where software development is going, with a habit of exploring emerging AI tools, frameworks, and workflows and applying the useful ones in practice.
• Hands-on experience building software, automations, integrations, or AI-powered applications using real business data in production systems.
• Experience with REST APIs, webhooks, authentication, third-party systems, databases, and reliable data flows.
• Experience owning software through production, including source control, environment management, automated testing, CI/CD, deployment workflows, monitoring, and production reliability.
• Practical experience integrating LLM APIs into production software using structured outputs, retrieval, tool use, context management, and safeguards.
• Ability to test AI behavior, identify failure modes, debug code and workflows, and improve reliability rather than accepting model output at face value.
• Sound judgment about when to use AI, traditional code, automation, or low-code, and how to combine them effectively.
