Hire RAG engineers who ground every answer in your data.
Retrieval specialists who turn a pile of documents into answers you can trust, with hybrid search, reranking, and citation grounding so the system has a real source behind every claim.
At a glance
Retrieval specialists who ground every answer in your data.
Review the work RAG & Retrieval Engineers can own, the skills required, and the systems they can help you build.
- Who this is for
- Engineering leaders who need an engineer to work within their existing team.
- Topics
- Vector DBs
- Hybrid search
- Reranking
- LlamaIndex
- pgvector
- Qdrant
What they own.
- Design retrieval-augmented pipelines over your own content
- Build hybrid search that blends meaning and keywords
- Add reranking and citation grounding so answers stay honest
- Tune chunking and ingestion for messy, real-world documents
- Stand up and scale vector infrastructure
- Measure retrieval quality with real evals, not vibes
What you can ship with them.
Tools they reach for in production.
- Search
- Vector DBsHybrid searchRerankingLlamaIndexpgvectorQdrant
- Ingest
- ChunkingOCRNLP pipelines
- Ground
- CitationsKnowledge graphs
- Stack
- PythonEmbeddingsClaude / GPT
Seniority: Engineers who have made retrieval work on real, messy corpora.
Five stages.
The top 3% remain.
Every stage asks the same question: can they keep AI running once real customers are using it? Getting something started is the easy part, and it is not what we screen for.
400 applicants
3%of applicants reach
your shortlist
What removes them
We start with something they built
100% → 20%One real system, pressed hard. How much traffic did it take? What broke first? Who got the call when it did?
We break something and watch them fix it
20% → 9%A working system with a bug hidden inside it. Anyone can build a demo in a weekend. Fixing code you have never seen is the actual job.
How will you know it is working?
9% → 5%Before they write anything, they have to tell us how they would test it, and what they would do when it gets an answer wrong.
Make it fast without running up the bill
5% → 4%We give them a speed target and a budget, then ask them to explain the tradeoffs they made to hit both.
It is late and the AI got it wrong
4% → 3%What do you do first? How do you find out what happened, undo it, and explain it to the customer in plain words?
Often hired together.
Generative AI Engineers
All-rounders who ship a whole AI feature end to end, or anchor a pod of the specialists below.
Data Engineers for AI
The pipelines that turn raw data into retrieval-ready, training-ready fuel.
LLM Engineers
Model specialists who fine-tune, quantise, and serve the model itself.
Find the people to accelerate your roadmap.
You don’t need more resumes. You need proven AI engineers embedded in your workflow and ready to build from day one. Tell us what’s missing and we’ll line up a shortlist.