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AI Internal Wiki Search Relevance Engineer

Evelyn Vestergaard

Full-time · Job · Los_Angeles

About the role

Have you ever tried searching your own company's internal wiki and given up after three tries? That's the problem our own support engineers deal with daily. We don't care where you work from. We care about output, clear communication, and ownership. Tuvex Solutions builds AI-powered knowledge base tools for customer support teams, and our own internal wiki search is embarrassingly bad compared to what we ship for clients. You'll build a RAG-based search layer over our internal documentation, working with our support engineering team to make sure it actually surfaces the right answer, not just a keyword match.

Responsibilities

  • Build a RAG-based search system over internal wiki documentation
  • Fine-tune retrieval relevance based on real support team query patterns
  • Integrate search results into existing internal tooling
  • Gather feedback from support engineers to iterate on relevance
  • Document the retrieval pipeline for future contributors

Requirements

  • Experience with RAG architectures using LangChain or similar
  • Hugging Face experience for embedding model selection
  • Python fundamentals for building retrieval pipelines
  • Interest in search relevance and information retrieval problems
  • 0-2 years experience welcome

Benefits

  • Fully remote
  • Health insurance
  • Flexible hours
  • Home office stipend
  • Mentorship from the support engineering team

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Categories

NLP & Prompt Engineering Automation & AI Workflows

Skills

python rag langchain hugging face

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Job Type

Full-time

Level

Junior

Language

English

Salary Range

$60k – $80k / year

AI Expertise

NLP & Prompt Engineering Automation & AI Workflows

Skills

python rag langchain hugging face

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