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RAG Engineer

TechnicalExplosive GrowthHiring since 2024

Builds and optimizes RAG pipelines connecting LLMs to enterprise knowledge bases — designing vector search, document chunking, and retrieval logic so AI answers with real company data instead of hallucinating. Every enterprise with proprietary data (banks, media, healthcare, law) needs this role. Australia's Big Four banks and Singapore's financial institutions are the largest APAC employers.

Salary Range
$120K–$200K+
Hiring Companies
Pinecone · Microsoft · ANZ Bank
Hot Regions
USAAustraliaSingaporeU.K.India
Core Skills
Vector database · Embedding · LLM

Learn this at JR Academy

Real bootcamps currently running on jiangren.com.au — click through for the full outline and enrollment

AI Engineer Fundamentals: RAG BasicsEntry-level · Vector DB + Embedding + Retrieval pipeline
View Course →
Use RAG to Improve GenAI Responses WorkshopWorkshop · RAG hallucination control + retrieval tuning
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AI Engineer Advanced: RAG → Agent → Fine-tuningAdvanced · Advanced retrieval + Agent orchestration + Fine-tuning
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Job Description Analysis

Core Responsibilities

  • Design and build enterprise-grade RAG pipelines from ingestion through generation
  • Improve retrieval quality through chunking strategy, embedding selection, hybrid retrieval, and reranking
  • Connect enterprise knowledge sources such as Confluence, SharePoint, and internal wikis across multiple file formats
  • Build evaluation frameworks for recall, precision, answer quality, and hallucination control
  • Work with security teams on document-level permissions so users only retrieve what they are allowed to see

Requirements

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  1. Proficient in the deployment and tuning of at least one vector database (Pinecone, Weaviate, Qdrant, Milvus)
  2. In-depth understanding of Embedding models (OpenAI, Cohere, BGE) and their performance differences in different languages/domains
  3. 3+ years of Python/TypeScript engineering experience, familiar with LangChain or LlamaIndex
  4. Master document processing technologies: PDF parsing, OCR, table extraction, Markdown conversion
  5. Have experience in production environment deployment, able to handle performance and cost optimization of millions of documents
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Nice to Have

  • Experience with traditional search engines such as Elasticsearch or Solr
  • Learn about GraphRAG and Knowledge Graph Enhanced Search
  • Familiar with the document structure and compliance requirements of financial/legal/medical and other industries

A Day in the Life

  • Morning: Review overnight sync logs and fix ingestion failures before they affect answer quality
  • Morning: Debug user-reported bad answers and determine whether the failure came from retrieval or generation
  • Afternoon: Compare chunking strategies such as paragraph, semantic, and sliding-window approaches
  • Afternoon: Work with security to enforce retrieval permissions based on document tags and access rules
  • Evening: Benchmark new embedding models on real multilingual document sets

Career Transition Guide

Suitable Backgrounds

  • Search engine/information retrieval engineer: core skills are highly overlapping and can be transformed quickly
  • Back-end engineer: Have experience in data pipeline and API development, just supplement with knowledge of Embedding and vectorsSee this career today: Software Engineer
  • Data Engineer: Familiar with ETL and data processing, the document processing pipeline is a natural extensionSee this career today: Data Engineer
  • NLP Engineer: With a foundation in text processing and semantic understanding, you can directly enter the deep water area.

Transition Path

  1. Step 1: Learn the Embedding principle and the basics of vector similarity search, and complete the introductory tutorial on vector databases
  2. Step 2: Use LangChain + Pinecone to build a simple question and answer RAG prototype
  3. Step 3: Learn more about chunk strategies, hybrid retrieval (keywords + vectors) and re-ranking techniques
  4. Step 4: Optimize the RAG pipeline on the real data set and establish evaluation benchmark (recall rate, NDCG)
  5. Step 5: Complete a production-level RAG project (such as an enterprise knowledge base Q&A) and prepare an interview portfolio

Learning Roadmap

Phase 1: Basics (1-2 months)

Understand the Embedding principle, vector spaces and similarity measuresLearn basic operations of vector database (CRUD, index, query)Build the first RAG prototype project with LangChain

Phase 2: Core Technology (February-April)

In-depth Chunk strategy: fixed window, semantic slicing, recursive slicingMaster hybrid retrieval: BM25 + vector retrieval + rerank (Cohere Rerank)Learn multi-format document processing: PDF, Word, HTML, image OCRImplement the RAG evaluation framework: tools such as RAGAS and TruLens

Phase 3: Production (April-June)

Build a RAG system with millions of documents to optimize performance and costImplement permission control, incremental synchronization and caching strategiesExplore GraphRAG and Agentic RAG high-level architecture

Common Mistakes

Wrong

I think RAG is just throwing documents into a vector database and then querying them

Right

Each step of Chunk strategy, Embedding selection, and retrieval reranking affects the final quality and requires fine tuning.

Wrong

Only use vector search, ignore keyword search

Right

Hybrid retrieval (vector + BM25) performs better than pure vector retrieval in most scenarios

Wrong

Go online without doing evaluation, and judge the effect by manually testing a few questions

Right

An automated evaluation set must be established to continuously monitor recall and accuracy rates.

Recommended Resources

AI Engineer Learning PathPrompt Master FundamentalsAI Data AnalysisAI Career Impact Map

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