RAG Engineer
- Full-time
- Senior
- Remote (worldwide)
Senior remote RAG Engineer at MacroCoderz: design retrieval-augmented generation systems - chunking, hybrid search, reranking and evaluation - at scale.
Role at a glance
| Employment type | Full-time |
|---|---|
| Level | Senior |
| Experience | 4+ years |
| Location | Remote (worldwide) |
| Team | AI engineering |
| Education | Degree in computer science or equivalent practical experience |
| Posted | 10 October 2026 |
About the role
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 recall@k 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.
Skills and tools
- Python
- RAG
- Vector databases
- Hybrid search
- Reranking
- Embeddings
- Information retrieval
- Evaluation
Who this role suits
Search engineers, information retrieval specialists and senior backend engineers who have gone deep on RAG.
The work behind this role
RAG Engineer: common questions
Yes. The RAG Engineer role at MacroCoderz is fully remote and open to applicants worldwide. It is a full-time, senior position.
4+ years (senior). Education: Degree in computer science or equivalent practical experience. Who it suits: Search engineers, information retrieval specialists and senior backend engineers who have gone deep on RAG.
A RAG Engineer needs strong Python, hands-on experience with embeddings and vector databases, and an understanding of keyword search, dense retrieval, hybrid search and reranking. Just as important is the ability to measure retrieval quality with metrics such as recall@k, so that changes are proven rather than guessed.
Use the application form on this page. It asks for your contact details, city, LinkedIn profile, expected salary, how you heard about the role, a short description of two or three projects you have worked on, and your CV as a PDF or Word file. An engineer reviews every application and emails shortlisted applicants to arrange a call.