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Senior RAG Engineer

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Job Title: Senior Retrieval / RAG Engineer
We’re working with a fast-growing AI company looking for a Senior Retrieval / RAG Engineer to take ownership of the retrieval layer at the heart of its production AI platform. You’ll own everything from document ingestion, chunking, and embeddings through to hybrid search, reranking, evaluation, and agentic retrieval. This is a highly hands-on role where you’ll have real influence over architecture and technical direction, building AI systems that need to retrieve the right information accurately and reliably at scale. Fully remote anywhere within the EU.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Must Haves
- 5+ years of production backend engineering experience
- 2+ years building and operating RAG / retrieval systems in production
- Strong Python experience
- FastAPI or similar production Python frameworks
- Production experience with vector databases such as Qdrant, Pinecone, Weaviate or similar
- Strong understanding of embeddings, semantic search, hybrid retrieval, and reranking
- Experience evaluating retrieval quality using metrics such as Recall@K and NDCG
- PostgreSQL / SQL
- Experience diagnosing and improving retrieval performance in production
- Comfortable taking genuine end-to-end ownership


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Nice to Haves
- Agentic retrieval and multi-step search workflows
- Query decomposition
- BM25 / information retrieval experience
- Golden datasets and retrieval regression testing
- Large-scale or OCR-heavy document ingestion
- Redis and background processing
- Multi-tenant architectures and data isolation
- Multilingual retrieval/search
- Experience benchmarking different embedding models, vector databases, or LLM approaches
- Experience using AI coding tools such as Claude Code, Copilot or similar
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
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