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AI Knowledge Base Chunking Strategy Developer

Owen Goldberg

Full-time · Job · Chicago

About the role

RAG performance lives or dies on chunking strategy. Fixed-size character chunks miss sentence boundaries and break coherent thought units. Sentence-level chunks lose context that spans paragraphs. Semantic chunking is more principled but computationally expensive and requires a model that understands the content domain well enough to identify coherent segments. The right chunking strategy varies by content type, query distribution, and the embedding model used for retrieval. We build enterprise RAG infrastructure for 40 clients with knowledge bases ranging from 100,000 to 50 million documents. Chunking strategy is the configuration decision with the largest empirical impact on retrieval quality, and we've been using fixed strategies per deployment instead of optimizing per client. The opportunity is to build adaptive chunking: automatically selecting and tuning the chunking approach based on the content type and observed retrieval quality. You'll build the chunking strategy optimization system: implementing multiple chunking approaches (fixed, sentence, semantic, hierarchical), building the retrieval quality evaluation that compares them empirically per deployment, and designing the adaptive selection logic that chooses the best strategy per content type and tunes its parameters based on production retrieval feedback.

Responsibilities

  • Implement diverse chunking approaches: fixed-size, sentence-boundary, semantic, and hierarchical
  • Build retrieval quality evaluation comparing chunking strategies on representative query-answer pairs
  • Design adaptive chunking selection choosing the best approach per content type and query distribution
  • Implement chunk parameter tuning optimizing overlap and size for retrieval performance
  • Create chunking evaluation benchmarks clients use to validate RAG performance improvements
  • Monitor production retrieval quality and trigger chunking reoptimization when quality drifts

Requirements

  • 3+ years in NLP engineering with RAG, information retrieval, or LLM application development
  • Strong Python and HuggingFace skills with embedding model and vector database experience
  • Deep understanding of chunking strategy trade-offs and their impact on retrieval quality
  • Experience designing A/B evaluation frameworks for information retrieval systems
  • Ability to build optimization systems that operate without requiring manual tuning per deployment

Benefits

  • Fully remote with flexible schedule
  • Health and dental insurance
  • $3,000 annual professional development budget
  • 401(k) with match
  • Equity options

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Categories

NLP & Prompt Engineering MLOps & AI Infrastructure

Skills

python pytorch hugging face rag fastapi

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

NLP & Prompt Engineering MLOps & AI Infrastructure

Skills

python pytorch hugging face rag fastapi sql

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