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AI Enterprise Search Relevance Tuning Lead

Dominic Johansson

Full-time · Job · London

About the role

The models you'll ship will be used by knowledge workers at hundreds of enterprise customers every time they search their internal wikis, tickets, and contracts. We need a lead who can own relevance end to end: retrieval quality, reranking, and the RAG pipeline that turns search results into grounded answers. You'll set the technical direction for how we evaluate relevance across dozens of customer document sets that look nothing alike, and you'll have the final call on tradeoffs between latency, cost, and answer quality. This is a hands-on lead role — you'll be writing code and reviewing architecture in the same week.

Responsibilities

  • Set technical direction for retrieval, reranking, and RAG answer generation
  • Own the relevance evaluation framework used across all customer deployments
  • Review architecture decisions from the broader search and NLP team
  • Partner with customer engineering to diagnose relevance regressions on live accounts

Requirements

  • Deep hands-on experience building production RAG systems with LangChain or similar
  • Strong understanding of retrieval and reranking tradeoffs at enterprise scale
  • Comfortable deploying and debugging containerized services with Docker
  • Experience leading technical direction on a small, senior team

Benefits

  • Fully remote, London-anchored team with flexible hours
  • Private health insurance
  • Generous professional development budget
  • 25 days holiday plus bank holidays

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Categories

NLP & Prompt Engineering MLOps & AI Infrastructure

Skills

python rag langchain hugging face docker

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

NLP & Prompt Engineering MLOps & AI Infrastructure

Skills

python rag langchain hugging face docker

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