/ TEAMS · Specialist engineering · RAG / Search
Source engineers experienced in Retrieval, hybrid search, ranking, corpus updates and evidence-grounded answers. for business-critical production systems, safe integrations and measurable technical ownership.
2,000+ engineers in our active talent network · Regional recruiters
/ SYSTEM STACK
retrieval evaluation
embeddings and vector stores
hybrid BM25 search
document chunking
reranking
/ AVAILABLE TO HIRE
Profiles show the experience we can source. Individual candidate availability is confirmed after enquiry.
Senior RAG / Search Engineer
Available14+ years of experience · Serbia
RAG / Search Engineer with 14 years in production systems, focused on retrieval evaluation and embeddings and vector stores.
Languages: Serbian, English C1
Mid-level RAG / Search Engineer
Available3+ years of experience · Lithuania
RAG / Search Engineer with 3 years in production systems, focused on embeddings and vector stores and hybrid BM25 search.
Languages: Lithuanian, English B2+
Mid+ RAG / Search Engineer
Available5+ years of experience · Slovakia
RAG / Search Engineer with 5 years in production systems, focused on hybrid BM25 search and document chunking.
Languages: Slovak, English B2+
Senior RAG / Search Engineer
Inquire availability7+ years of experience · Croatia
RAG / Search Engineer with 7 years in production systems, focused on document chunking and reranking.
Languages: Croatian, English B2+
/ Engineering teams
Onectus uses its active engineering network and regional recruiting partners to search against concrete systems, environments and responsibilities. We evaluate practical experience rather than treating a framework mention or certification as proof of delivery. Particular availability and engagement terms are confirmed after an enquiry. For this role, practical knowledge of retrieval evaluation, embeddings and vector stores, hybrid BM25 search helps define the engineering search.
/ Technical specializations
The right profile depends on your actual architecture, performance constraints, integration boundaries and operational responsibilities.
RAG / Search responsibilities include retrieval evaluation, related embeddings and vector stores, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
RAG / Search responsibilities include embeddings and vector stores, related hybrid bm25 search, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
RAG / Search responsibilities include hybrid bm25 search, related document chunking, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
RAG / Search responsibilities include document chunking, related reranking, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
RAG / Search responsibilities include reranking, related grounded citation generation, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
RAG / Search responsibilities include grounded citation generation, related retrieval evaluation, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
/ Project scenarios
Improve low-recall knowledge search. Define constraints around retrieval evaluation, map dependencies and test how the proposed implementation behaves under real operating conditions.
Build permission-aware retrieval. Define constraints around embeddings and vector stores, map dependencies and test how the proposed implementation behaves under real operating conditions.
Diagnose citation and grounding errors. Define constraints around hybrid bm25 search, map dependencies and test how the proposed implementation behaves under real operating conditions.
Reindex changing document corpora. Define constraints around document chunking, map dependencies and test how the proposed implementation behaves under real operating conditions.
/ Practical evaluation
A senior engineering interview should surface directly relevant implementation details, debugging history and operational judgment.
Describe a production issue involving retrieval evaluation and what evidence led to a fix.
Which decisions around embeddings and vector stores and hybrid BM25 search were you personally accountable for?
Describe a production issue involving hybrid BM25 search and what evidence led to a fix.
Which decisions around document chunking and reranking were you personally accountable for?
/ Sourcing process
Define platform versions, deployment constraints, traffic patterns, required integrations and ownership.
Search the active network and partner channels for platform-specific engineering experience.
Discuss real systems, code or configuration decisions, troubleshooting and change control.
Confirm role boundaries, location or remote access, availability, security and interviews.
Profiles, availability and engagement terms are confirmed for each request.
/ Questions
Yes. Tell us the relevant versions, operational environment and the responsibilities connected to retrieval evaluation. We qualify hands-on experience during each search.
These can be included when central to the project. We distinguish practical implementation work from general familiarity and ask about failure scenarios and testing.
We look at production decisions, trade-offs, incident diagnosis, code or infrastructure ownership and the ability to explain decisions without relying on a technology checklist.
Share your architecture, technical constraints, collaboration model and priorities. We can then discuss relevant sourcing channels, evaluation criteria and next steps.
/ Onectus · TEAMS
Tell us the technology, production responsibilities, experience level and working model. We'll activate the appropriate engineering sourcing channels.
