Retrieval work rewards search engineering experience, and Chinese consumer platforms run search at a scale that teaches it fast.
Good retrieval-augmented generation is mostly not about the language model. It is chunking, embedding choice, hybrid lexical-and-vector search, reranking, and the evaluation harness that tells you whether last week’s change actually helped. Those are search engineering problems.
Engineers who have worked on search or recommendation at Alibaba, Baidu, ByteDance or Meituan have met every one of them under latency budgets far tighter than any enterprise assistant imposes. They also tend to have met them at a scale where a bad ranking decision is measurable in revenue within the hour, which produces a useful suspicion of intuition.
Milvus, created by Zilliz, is one of the most widely deployed vector databases in the world and a graduated CNCF project, and the engineering community around it is substantially Chinese. If you want somebody who chooses an index rather than accepting the default — IVF against HNSW against DiskANN, what quantisation costs in recall, how a collection behaves once it stops fitting in memory — this is a pool where that knowledge is ordinary rather than exotic.
That matters more than it sounds. Most retrieval systems that plateau do so because nobody revisited the index configuration after the corpus grew by an order of magnitude.
Chinese has no spaces between words, so segmentation and tokenisation have been first-order concerns in Chinese search for decades rather than implementation details somebody else handles.
Engineers from that tradition reason about the tokeniser instead of accepting it, which is exactly the instinct you want when retrieval is failing on your product names, your part numbers, your legal citations or your abbreviations — the cases where English retrieval quietly breaks too. And if any part of your corpus is Chinese, Japanese or Korean, this stops being a transferable habit and becomes the specific skill you need.
Ask what they would try first when retrieval returns passages that are plausible but wrong. The answer tells you almost everything.
Strong candidates go to the retrieval step before the model: chunk boundaries splitting a fact from its context, an embedding model that does not fit the domain, a term that lexical search would have caught and vector search missed, a reranker that is not earning its latency. Weak candidates reach for a larger model or a longer prompt, which is the expensive way to not fix it.
Then ask how they knew a change was an improvement. Anyone who has run retrieval in production has a golden set and a scoring method; anyone who has not will describe trying things and reading a few outputs.
Retrieval is now the default answer to every question about grounding a model, and it is often the wrong one. If the knowledge is small and stable, a well-constructed prompt is cheaper and more predictable. If the task needs a format or a style reliably, fine-tuning addresses it and retrieval does not. If the answer requires aggregating across thousands of documents rather than finding a few, you want a query against a database, not a similarity search.
Ask a candidate to describe a problem where they decided against retrieval. Somebody who has built several systems will have an example and will be slightly pleased to be asked. Somebody who has built one will treat the question as a trap. The first is the hire: an engineer who reaches for retrieval regardless will build you something that works adequately and costs more than it should for years.
Retrieval work decomposes well across a time difference. Indexing, embedding experiments and evaluation runs are asynchronous by nature, and four hours of overlap is normally sufficient for the conversations that do need to happen live — mostly the ones where somebody has to look at bad results with you.
The real constraint is corpus access, and it is worth resolving before anything else: your documents are the asset, and a contractor cannot tune retrieval against documents they may not read. A representative, minimised subset is usually enough to develop against. Our note on IP assignment with Chinese contractors covers how the contract should treat the corpus and the embeddings derived from it, and the overlap note covers the hours.
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Our guide to hiring RAG engineers in the UK covers what the domestic market pays, how to screen, and the interview questions that actually discriminate.
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