AI Software Engineer

AI Engineering & Delivery

• Design, develop, test, and maintain AI-enabled platform capabilities, APIs, services, and reusable software enhancements.
• Translate product requirements, implementation feedback, and technical design direction into scalable, maintainable, and production-ready systems.
• Review feature requests from Solution Engineering and support development of reusable platform capabilities where requirements should become product or platform enhancements.
• Build capabilities that can be leveraged across multiple customer implementations, workflows, and platform experiences.
• Write clean, well-structured, testable, and maintainable code that supports long-term platform quality and team velocity.

Modern Development & AI-Enabled Engineering

• Use AI-assisted development practices to accelerate coding, testing, documentation, refactoring, and engineering analysis.
• Apply LLM-enabled workflows responsibly while maintaining strong code quality, security, review practices, and maintainability.
• Contribute to reusable engineering patterns, documentation, and development practices that improve team velocity and consistency.
• Use AI-enabled tools where appropriate to support test generation, code review preparation, technical documentation, and developer productivity.

Architecture, Platforms & Interoperability

• Build APIs, services, prompt patterns, agent integrations, extraction workflows, and integration patterns that support platform interoperability.
• Develop backend services and API-enabled capabilities using technologies such as Python, JavaScript, Node.js, and related frameworks.
• Collaborate with Application Engineering, Solution Engineering, Platform Reliability, and Solution Architecture to align technical decisions across the platform.
• Promote reusable components, shared services, consistent data contracts, and integration standards.
• Support the appropriate balance between low-code platform implementation, custom engineering, reusable AI capabilities, and API-enabled services.

Data Science & AI Integration

• Support model integration, prompt engineering, retrieval patterns, advanced extraction logic, AI quality measurement, and agent configuration.
• Ensure AI-driven features are production-ready, scalable, cost-effective, secure, and aligned with platform needs.
• Partner with Solution Engineering on complex document types, failed extractions, prompt optimization, reusable implementation patterns, and AI quality improvements.
• Help package mature AI capabilities, prompts, evaluation methods, and configuration guidance so they can be reused across customer implementations.
• Collaborate with Data & AI Engineering peers to ensure AI outputs, extracted data, and model-driven capabilities are reliable, testable, traceable, and understandable to users.

DevOps, Reliability & Security

• Contribute to CI/CD pipelines, automated testing, deployment automation, monitoring, and incident readiness.
• Build software with strong attention to reliability, performance, availability, scalability, and operational support.
• Support observability practices including logging, monitoring, error handling, and production diagnostics.
• Follow secure development practices aligned with internal security standards, SOC 2 Type II, ISO 27001, and enterprise security expectations.
• Ensure AI-enabled capabilities are developed with appropriate attention to data handling, access control, auditability, and responsible AI practices.

Collaboration & Technical Ownership

• Work effectively with Product, Solution Engineering, Application Engineering, Platform Reliability, Solution Architecture, and cross-functional stakeholders.
• Take ownership of assigned features, services, defects, and technical improvements from design through production support.
• Communicate technical tradeoffs clearly and contribute to pragmatic engineering decisions.
• Support knowledge transfer to Solution Engineering and Application Engineering where AI capabilities become reusable implementation or application patterns.
  • Contribute to a culture of accountability, collaboration, continuous improvement, and delivery excellence.