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AI Engineer

Job

RAPID EAGLE INC

Minneapolis, MN (In Person)

Full-Time

Posted 1 week ago (Updated 1 day ago) • Actively hiring

Expires 7/23/2026

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Job Description

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: o Frontend/backend for RAG and app work: Next.js and NestJS (Node) o Light work with data pipelines: Python; Snowflake as the data platform (medallion architecture: bronze/silver/gold) Tools and AI coding assistants: o Claude Code o GitHub Copilot via Visual Studio o Evaluating vendor AI tools (e.g., Snowflake AI, Domo AI); use-case dependent.
o 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:
o Efficient interaction patterns with LLMs (token management, sending full articles vs. selective context) o Agentic approaches for complex reasoning (e.g., applying AP style guide across thousands of rules) o 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: o Building ingestion/ETL/ELT pipelines (Python) o Working with Snowflake; experience in similar platforms (Redshift, Synapse) acceptable with ability to translate principles. o 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.