AI Native Software Engineer

Greenway Health seeks an AI Native Software Engineer in Bangalore, India, to help move its software development process toward an AI-first model. The engineer will use AI tools as a primary development partner while retaining responsibility for the correctness, security, maintainability and production readiness of the final software.

Responsibilities

• Translate product, business and technical requirements into precise prompts, implementation plans and acceptance criteria. Use AI tools to produce 80–90% of initial solution drafts, then review, validate and refine them.
• Use AI-assisted tools for coding, design, testing, documentation, debugging and refactoring. Identify hallucinations, inaccurate assumptions, invalid code, missing logic and edge cases, security issues and mismatches with requirements.
• Create automated tests to verify expected behavior and business outcomes. Ensure generated code meets company standards for architecture, performance, security, maintainability and documentation.
• Build reusable prompt templates and AI-native workflows. Document prompts, assumptions, validation steps, risks and implementation decisions; collaborate with product, QA, DevOps, architecture and security teams, and guide other engineers in adopting AI-native practices.

Requirements

• A strong software engineering background and experience building production-grade applications, using AI coding tools or large language models for software development, and working in at least one modern programming language. No minimum years of experience or education requirement is specified.
• Understanding of APIs, databases, cloud services, testing, CI/CD and secure coding. Ability to write precise prompts, critically evaluate AI-generated output, debug, review code, solve problems, communicate clearly and take ownership of final deliverables.

Preferred experience:

• GitHub Copilot, Cursor, ChatGPT, Claude, Gemini, Amazon Q Developer or similar tools; prompt libraries, AI development workflows or internal engineering automation.
• LLM applications, AI agents, retrieval-augmented generation or workflow orchestration; leading process transformation or helping engineering teams adopt new practices; and knowledge of AI governance, data privacy, security and responsible AI use.