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Trebecon LLC

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

Career Insights for Artificial Intelligence Engineer (General)

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What they do

An Artificial Intelligence Engineer develops, tests, and deploys artificial intelligence models. May work closely with data software engineers and data professionals to train and implement AI models into existing systems or develop new applications.

$137,984 / year median in Minnesota

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Job Description

Role :
Senior AI-Native Full-Stack Engineer Location:
Minnesota -
Local Candidates Only Job Overview:
Looking for a Senior AI-Native Full-Stack Engineer with strong experience in modern full-stack development and AI-powered coding tools. The candidate should be comfortable using tools like Codex, GitHub Copilot, Claude Code, or similar AI coding agents to build, test, document, and deliver production applications. Strong AI coding/agent experience is required
Required Skills:
7+ years of software engineering experience 4+ years of full-stack development experience Strong React / Angular experience Strong JavaScript, TypeScript, HTML, CSS Experience with REST APIs, integrations, and middle-tier services Java 17+ / Spring Boot experience preferred Experience with PostgreSQL, MySQL, Oracle, Cosmos DB , or similar databases Experience with Azure, AWS, or Google Cloud Platform Strong CI/CD and Agile/Scrum experience Hands-on experience with AI coding tools such as Codex, GitHub Copilot, Claude Code, or similar Ability to review and validate AI-generated code for security, performance, functionality, and maintainability Strong stakeholder communication and problem-solving skills Experience converting business requirements into user stories, specifications, acceptance criteria, and technical solutions
Preferred:
Spec Kit / BMAD / agentic development workflows Docker / Kubernetes Automated testing and observability Production support experience Enterprise or regulated-environment experience
Key Responsibilities:
Develop end-to-end full-stack applications using AI-native development practices Work directly with business stakeholders to understand requirements and build prototypes Create specifications and AI-executable tasks Review, test, and improve AI-generated code Manage delivery from discovery through production Support Agile ceremonies, demos, releases, and production deployments Develop reusable prompts, specifications, instructions, and testing patterns