RAG Engineer

MacroCoderz · Lahore, Pakistan

FULL TIMEPKR1.2 – PKR2.4 / yr

Job Description

Senior remote RAG Engineer at MacroCoderz: design retrieval-augmented generation systems - chunking, hybrid search, reranking and evaluation - at scale.

Retrieval-augmented generation is the core of most AI systems we ship: an assistant is only as good as the passages it retrieves. As a RAG Engineer you own that retrieval layer for client systems with thousands to millions of documents.

You decide how documents are parsed and chunked, which embedding models and vector stores to use, when to combine keyword and vector search, and how to rerank. Then you prove each decision with retrieval and answer-quality metrics.

What you will do

Architect RAG pipelines: ingestion, parsing, chunking, embedding, indexing and retrieval.
Implement hybrid search, metadata filtering, reranking and multi-tenant access control.
Build retrieval and answer-quality evaluation, and run experiments against it.
Optimise latency and cost for large document sets.
Review the retrieval work of other engineers and set standards across projects.

What we are looking for

4+ years of software engineering, including production search or retrieval systems.
Deep hands-on experience with embeddings and at least one vector database.
Understands BM25, dense retrieval, hybrid search and reranking, and when each wins.
Experience measuring retrieval quality with metrics such as and MRR.
Strong Python.

Nice to have

Experience with pgvector, Qdrant, Weaviate, Pinecone, Elasticsearch or OpenSearch.
Document parsing for PDFs, tables and scanned files.
Knowledge graphs or GraphRAG.

Location

: fully remote, open to applicants worldwide.

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