AI Native Full Stack Engineer -- W2 Profiles -- Onsite

Role: Sr. Full Stack Engineer – AI Native Development

Location: Minnesota - Onsite

Required Skills & Experience

• 7+ years professional software engineering / delivery experience
• 4+ years full-stack application development
• Strong experience building modern UX, delivering features end-to-end
• Production experience with AI coding agents: Codex, GitHub Copilot, Claude Code, or comparable
• AI-first development mindset — able to create specs, provide effective context to AI agents, decompose work, review AI-generated output, redirect agents when incomplete
• Strong experience with React, Angular, or comparable frameworks
• Proficiency: JavaScript ES6+, TypeScript, HTML, CSS
• Middle-tier services: REST APIs, application integration, data access, service orchestration
• Familiarity with Java 17+, Spring Boot or comparable middle-tier tech
• Databases: PostgreSQL, MySQL, Oracle, Cosmos DB
• Cloud: Azure, AWS, or Google Cloud Platform
• Automated CI/CD delivery
• 5+ years Agile/Scrum delivery experience
• Ability to translate business outcomes into user stories, acceptance criteria, specs, and AI-executable tasks
• Strong stakeholder communication, facilitation, demo, and problem-solving skills
• Ability to validate AI-generated solutions for functionality, security, performance, maintainability, architectural fit

Preferred Skills & Attributes

• Experience with specification-driven/agentic delivery methods (Spec Kit, BMAD, reusable agent instructions, multi-agent workflows)
• Rapid prototyping & stakeholder feedback incorporation
• Docker, Kubernetes, automated testing, observability, production support
• Enterprise-scale or regulated environment experience
• Familiarity with secure/responsible enterprise AI usage

Primary Responsibilities

1. Full-Stack Development – UIs, middle-tier services, integrations, data access, tests, documentation, deployment assets
2. Forward-Deployed Engineer (Mini-FDE) – discover business needs, build prototypes, demo solutions, gather feedback, support adoption
3. Agile Delivery – own delivery from discovery to production: refinement, planning, specs, incremental delivery, demos, release support
4. AI-Generated Software Validation – review/correct AI output; ensure enterprise quality, security, accessibility, maintainability
5. Improve AIDLC Operating Model – build reusable specs, prompts, instructions, test patterns, context files, delivery practices