AI-Native Software Engineer / AI-DLC Developer

Quick Facts

• Contract (6–12 months)
• Hybrid: 3 days/week onsite (Chicago, IL / Edina, MN)
• Experience required: 8+ years

Description

Join a hands-on AI-DLC Developer / AI-Native Software Engineer role using AI to change how applications are designed, built, tested, evaluated, and delivered. You will take ambiguous business problems and rapidly turn them into working, validated prototypes and PoCs, then convert them into secure, maintainable, production-ready applications.

Responsibilities

• Build end-to-end applications using an AI-assisted development lifecycle (Understand Intent → Generate → Build → Evaluate → Refine → Deliver)
• Use AI throughout coding, testing, debugging, documentation, and refinement
• Develop applications, APIs, integrations, workflows, and automation, including AI/agentic solutions
• Create reusable development harnesses, templates, accelerators, prompts/context frameworks, test suites, and evaluation utilities
• Define acceptance criteria and continuously evaluate AI output for correctness, quality, security, usability, and business fit
• Collaborate with business/domain experts to demo, gather feedback, and iterate

Requirements

• 8+ years of strong hands-on software/application development, building real applications using AI-assisted/AI-native approaches
• Rapid prototyping and PoC development experience (often within days)
• Hands-on experience with tools such as Claude/Claude Code, GitHub Copilot, Cursor, OpenAI/Codex, Gemini, or Azure OpenAI
• Strong programming skills in Python, JavaScript/TypeScript, Java, .NET, or equivalent modern technologies
• Experience with APIs, integrations, databases, cloud services, CI/CD, automated testing, and application security
• Exposure to agentic AI, LLMs, RAG, tool calling, context engineering, and AI evaluation; experience building reusable evaluation harnesses/accelerators is highly desirable
• Strong software architecture, security, scalability, observability, and production engineering understanding

Benefits

• Opportunity to work in a non-traditional AI/ML lifecycle model focused on rapid AI-assisted delivery and continuous evaluation