Benefits:

1. 401(k) matching
2. Dental insurance
3. Health insurance
4. 
   
5. 
   

AI Engineer

Onsite

Minneapolis MN

Skills:-

Context

1. Role spans AI engineering
2. Tech decisions influenced by broader product stack:
3. Frontend/backend for RAG and app work: Next.js and NestJS (Node)
4. Light work with data pipelines: Python; Snowflake as the data platform (medallion architecture: bronze/silver/gold)
5. Tools and AI coding assistants:
6. Claude Code
7. GitHub Copilot via Visual Studio
8. Evaluating vendor AI tools (e.g., Snowflake AI, Domo AI); use-case dependent.
9. MCP servers: discussed; on roadmap; not currently required for internal LLM routing/abstraction.

Must Have Requirements

1. Strong Python experience (production-grade software engineering).
2. Hands-on experience working with LLMs in production (general LLM best practices; not strictly RAG). Examples:
3. Efficient interaction patterns with LLMs (token management, sending full articles vs. selective context)
4. Agentic approaches for complex reasoning (e.g., applying AP style guide across thousands of rules)
5. Practical strategies to avoid context overload and maintain relevance.
6. Ability to “run with projects,” operate independently, and collaborate with stakeholders.
7. Minimum experience: approximately 5 years; must have “done it before.”

Should Have

1. Familiarity with Next.js/NestJS/Node for application/RAG-related work; strong Python candidates can ramp with AI coding tools.
2. CI/CD experience; Terraform not required (team strength exists, can learn on the job).
3. Good culture fit: collaborative, mission-driven, able to navigate flexible stack choices aligned with product teams.

Could Have:

1. Exposure to data engineering concepts and tooling:
2. Building ingestion/ETL/ELT pipelines (Python)
3. Working with Snowflake; experience in similar platforms (Redshift, Synapse) acceptable with ability to translate principles.
4. Familiarity with medallion architecture and data modeling concepts is helpful but not strictly required (team can support ramp-up).

Additional Notes

1. RAG work currently lives in Next/Nest (Node); none in Python at present.
2. Preference for principles over specific vendor experience; candidates with adjacent platform knowledge can adapt.

Are you looking for more jobs nearby? Find your favorite jobs now by visiting our online jobs page.