AI Engineer

Benefits:

• 401(k) matching
• Dental insurance
• Health insurance

**AI Engineer

Onsite

Minneapolis MN

Skills:-

Context**

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

Must Have Requirements

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

Should Have

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

Could Have:

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

Additional Notes

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