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Blank Metal

AI Engineer

Minneapolis, MN (Remote)fulltimemidAdded 1 month ago

About this role

Blank Metal seeks an experienced AI Engineer to design and deploy production-grade AI systems for enterprise clients. You'll own end-to-end LLM integrations, architect scalable AI features, and work independently on complex projects that drive measurable business impact.

What you'll do

  • Design and implement production AI integrations from LLM API selection through deployment
  • Architect scalable, reliable AI-powered features using LLM frameworks and vector databases
  • Work independently on complex projects with minimal oversight
  • Leverage prompt engineering and cloud AI services to solve business problems
  • Ship production-grade code that balances quality with velocity
  • Mentor junior engineers and contribute to architectural decisions

What they're looking for

  • LLM APIs and frameworks (OpenAI, Anthropic, LangChain)
  • Full-stack development (React/modern frontend, Python/Node.js/C# backend)
  • System design and AI architecture
  • Vector databases and embedding models
  • Cloud platforms (AWS, GCP, or Azure)
  • API design and system integration
  • DevOps and deployment practices
  • Prompt engineering and RAG systems

Benefits

  • Ship AI that reaches production with real-world impact
  • Outcome-driven work aligned with client success
  • Collaborate with AI-native engineering culture
  • Balance quality craftsmanship with startup velocity
  • Open collaboration with shared ownership
  • Opportunity to own technical direction and mentor peers
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Blank Metal

Blank Metal builds AI systems and enables enterprise clients to redesign their workflows using large language models from leading providers like Anthropic and OpenAI. The company is hiring AI Engineers to design and deploy production-grade systems and Enablement professionals to drive client adoption and business outcomes through training and workflow optimization.

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Likely interview questions

  • Walk us through a production AI feature you shipped end-to-end. How did you approach LLM selection, integration, and deployment?
  • Describe your experience with LLM frameworks like LangChain. What patterns have you found most effective for production systems?
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