AI Native Software Engineer

Greenway Health is seeking an AI-Native Software Engineer to transform its software development process into an AI-first engineering model. The role uses AI tools to accelerate development while validating that solutions are reliable, secure, maintainable, and production-ready.

Responsibilities:

• Use AI-assisted development tools to generate, test, document, refactor, and improve software solutions
• Convert product and business requirements into clear technical prompts, implementation plans, and acceptance criteria
• Generate 80–90% of initial solution drafts using AI tools, then review, validate, and refine the output
• Detect and correct AI hallucinations, inaccurate assumptions, invalid code, security issues, and gaps in requirements
• Create automated tests to verify expected behavior and business outcomes
• Ensure all AI-generated code meets company standards for architecture, security, maintainability, performance, and documentation
• Build reusable prompt templates and AI-native development workflows for common engineering tasks
• Collaborate with product, QA, DevOps, architecture, and security teams to deliver production-ready solutions
• Document prompts, assumptions, validation steps, risks, and final implementation decisions
• Help educate and guide other engineers in adopting AI-native software development practices

Requirements:

• High School Diploma/GED equivalency
• Experience with GitHub Copilot, Cursor, ChatGPT, Claude, Gemini, Amazon Q Developer, or similar tools
• Experience creating prompt libraries, AI development workflows, or internal engineering automation
• Experience with LLM applications, AI agents, retrieval-augmented generation, or workflow orchestration
• Experience leading process transformation or helping engineering teams adopt new development practices
• Experience using AI coding tools or large language models for software development
• Strong software engineering background with experience building production-grade applications
• Proficiency in one or more modern programming languages
• Strong understanding of APIs, databases, cloud services, testing, CI/CD, and secure coding practices
• Ability to write precise prompts and evaluate AI-generated outputs critically
• Strong debugging, testing, code review, and problem-solving skills
• Ability to identify hallucinations, incorrect logic, missing edge cases, and gaps between generated code and requirements
• Excellent communication and documentation skills
• Strong sense of ownership for final deliverables, regardless of whether the initial code was AI-generated
• Knowledge of AI governance, data privacy, security, and responsible AI usage