Hire engineers who fix retrieval end to end.
Most LlamaIndex failures start at ingestion: a parser that flattened the tables, chunks that lost their headings, metadata nobody extracted. Fixing them means working the whole path (node parsing, index choice, query routing, response synthesis) and measuring retrieval separately from generation. Our engineers have shipped LlamaIndex systems in production, and harder stacks than it.
In short
Bigcircle staffs LlamaIndex engineers who fix retrieval where it usually breaks, at ingestion: parsers that keep tables and headings, metadata so a filter can do work similarity cannot, and a vector, keyword, or hybrid index chosen per corpus. Retrieval is scored apart from generation, so a prompt is not tuned to hide an indexing bug. Vetting starts from a pipeline they built, then flattens a table or drops a heading and watches them get the answer back.
Key facts
Definition
A Bigcircle LlamaIndex engineer fixes retrieval where it usually breaks, at ingestion: parsers that keep tables and headings, and an index chosen per corpus.
How Bigcircle does it
- Bigcircle starts vetting from a pipeline the engineer built.
- The exercise flattens a table or drops a heading, and Bigcircle watches the engineer get the answer back.
- On the engagement retrieval is scored apart from generation, so a prompt is not tuned to hide an indexing bug.
Comparison
A vector store chosen by default skips the ingestion path, while a Bigcircle LlamaIndex engineer keeps tables and headings and picks vector, keyword, or hybrid per corpus.
Key number
Bigcircle shortlists a LlamaIndex engineer in 2 to 5 days, and the top 3% of applicants remain after five stages.
At a glance
Retrieval engineers who work the ingestion path, not only the vector store.
See how a Bigcircle LlamaIndex engineer keeps tables and headings through ingestion, and why they score retrieval before they score the answer.
- Who this is for
- Engineering leaders who need an engineer to work within their existing team.
- Topics
- LlamaIndex
- Node parsers
- Query engines
- Vector
- Keyword
- Hybrid
What they own.
- Build ingestion pipelines where node parsing preserves the structure the answer depends on
- Extract and use metadata so filtering does work retrieval cannot do alone
- Choose between vector, keyword, and hybrid indexes per corpus instead of by default
- Route queries and decompose multi-part questions rather than retrieving once and hoping
- Tune response synthesis modes against a measured recall target
- Evaluate retrieval separately from generation, so the failing half is known
What actually breaks, and what they do about it.
The parser destroyed the answer
A table flattened into prose or a heading dropped during parsing removes the exact structure the answer depended on, and no amount of reranking recovers it. Ingestion is where most retrieval bugs are actually introduced, and where they have to be fixed.
Metadata nobody extracted
Filtering on document type, date or owner does work that semantic similarity cannot, and it is cheap. Corpora that skip metadata extraction end up retrieving the right topic from the wrong year.
One index for every question
Vector search misses exact identifiers users type verbatim; keyword search misses paraphrase. Choosing per corpus, and keeping both where the questions vary, is a decision that has to be measured rather than assumed.
Retrieval and generation measured together
When only the final answer is scored, a retrieval failure and a generation failure look identical, and teams tune the prompt to fix an indexing bug. Recall has to be measured on its own before generation quality means anything.
What you can ship with them.
Tools they reach for in production.
- Framework
- LlamaIndexNode parsersQuery engines
- Index
- VectorKeywordHybrid
- Stores
- pgvectorQdrantWeaviate
- Measure
- Recall@kFaithfulnessEval suites
Seniority: Engineers who have already worked on this stack, and on harder ones than it.
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?
Common questions
What does a Bigcircle LlamaIndex engineer own?
A Bigcircle LlamaIndex engineer builds ingestion pipelines where node parsing keeps the structure an answer depends on, extracts metadata so filtering can do work retrieval cannot do alone, and chooses vector, keyword, or hybrid indexes per corpus. They route queries, decompose multi-part questions, tune response synthesis against a measured recall target, and evaluate retrieval separately from generation.
How does Bigcircle vet LlamaIndex engineers?
Every Bigcircle LlamaIndex engineer has cleared five stages. Bigcircle starts with a system they already built, then breaks something and watches them fix it. For a LlamaIndex engineer the break is a parser that flattened a table or dropped the heading the answer depended on. They say how they would know it is working, how they would make it fast without running up the bill, and what they do first when it is late and the AI got it wrong. The top 3% of applicants remain.
How does hiring a Bigcircle LlamaIndex engineer start?
You tell us the AI problem, your stack, and the seniority you need. Bigcircle matches from LlamaIndex engineers who have already cleared vetting, and you see a shortlist in 2 to 5 days. You interview them, or run an optional paid trial on real work, before you commit. They embed in your repositories and your tools and own ingestion through response synthesis, with retrieval measured on its own. You own the code, and if the match is not working Bigcircle replaces them at no extra cost.
Where do Bigcircle LlamaIndex engineers look when retrieval fails?
Most LlamaIndex failures start at ingestion: a parser that flattened the tables, chunks that lost their headings, metadata nobody extracted. When only the final answer is scored, a retrieval failure and a generation failure look identical, and teams tune the prompt to fix an indexing bug. Bigcircle LlamaIndex engineers measure retrieval on its own before generation quality means anything.
Often hired together.
RAG & Retrieval Engineers
Engineers who measure retrieval apart from the answer. Chunking is part of the job, because a missing passage cannot be fixed in the prompt.
Data Engineers for AI
Engineers who keep a pipeline traceable from the source to retrieval and training. A retrieval bug is often a data bug, so the path is the job.
AI Agent Engineers
Engineers who stop a bad tool call before it runs. Every action is traced so it can be replayed.
Services and guides.
Services
Guides
- Why RAG systems fail in productionMost RAG failures look like model failures until retrieval, context assembly, and generation are measured separately. A plausible answer with a nearby citation can still be the wrong passage.
- Document intelligence beyond extraction: cross-document reasoning for agentsExtraction reads each document; a decision needs the whole packet. Before an agent acts on documents, resolve every party to one entity, reconcile the figures that should agree, check the packet is complete, keep a page and span on every value, validate in code, and pass a policy gate that decides between writing, review and asking for a missing document. Field accuracy per document says nothing about any of those steps, so evaluate the case, not the field.
- RAG vs fine-tuning vs agentic searchExhaust prompting first. Use retrieval when the model is missing a fact, fine-tuning when the facts are right and the format is wrong, and agentic search only when no single query can find the answer.
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.