Hire senior LlamaIndex developers for document AI and RAG
Senior engineers who use LlamaIndex Workflows, LlamaParse and retrieval engines to turn complex documents into reliable answers and automated processes.
By the Ryz Labs team · Updated October 2026
Hiring LlamaIndex developers through Ryz gets you senior Latin American engineers who build document-heavy AI systems: parsing messy PDFs, indexing them well and running agent workflows over the results. They are the top 1% of the engineers we interview, and they work on your team and in your repos within an hour of US time zones.
What our LlamaIndex developers work on
LlamaIndex has become the framework teams reach for when the hard part is the documents: 300-page filings, scanned contracts, spreadsheets with merged cells and tables that span pages. Its Workflows layer, which reached a stable 1.0 in June 2026, handles event-driven orchestration, and LlamaParse handles parsing. Typical projects:
- Document agents that read contracts, invoices or claims, extract fields into a schema and route exceptions to a human reviewer.
- Question answering over research reports, filings or manuals with citations to the exact page and table.
- Parsing pipelines using LlamaParse or open-source parsers, comparing accuracy on your own document set before choosing.
- Event-driven Workflows with branching, retries, human-in-the-loop steps and deployment as services.
- Index designs that combine vector, keyword and structured metadata retrieval, with sub-question and routing query engines.
- Connecting data through LlamaHub readers for SharePoint, Google Drive, Confluence, databases and object storage.
Skills we vet for
- Parsing and ingestion. Handling tables, multi-column layouts, images and scanned pages, and validating parse quality before indexing.
- Node and index design. Node parsers, chunk sizes, hierarchical and sentence-window approaches, and metadata that supports filtering.
- Retrieval and query engines. Retrievers, rerankers, router and sub-question engines, and hybrid search across vector stores.
- Workflows. Typed events, steps, context, concurrency and checkpointing, and testing workflows in isolation.
- Structured extraction. Pydantic schemas, validation and confidence handling when a field cannot be found.
- Vector store integrations. pgvector, Pinecone, Qdrant, Weaviate and others, with the tradeoffs of each.
- Evaluation. Retrieval metrics such as hit rate and MRR, faithfulness checks and labeled question sets.
- Model choice. Using Anthropic, OpenAI or open models through LlamaIndex integrations and swapping them under eval.
How we vet LlamaIndex developers
Recruiters source engineers who have shipped document AI and RAG systems, then our in-house ARC system ranks the pipeline. Candidates take structured NTRVSTA AI interviews on ingestion design, retrieval quality and workflow orchestration. Recruiters review each candidate before and after and send you a curated shortlist. AI scores are advisory. Humans decide.
Sample interview topics
- Your RAG system answers questions about revenue wrong because the numbers sit in a table split across two PDF pages. How do you fix ingestion?
- Design a Workflow that extracts 40 fields from a loan package, asks a human about low-confidence fields and resumes afterward.
- When would you use a sub-question query engine instead of a single retriever, and what does it cost in latency?
- How do you measure whether a new chunking strategy is better, and how many labeled questions do you need?
- Compare a managed parsing service with an open-source parser for documents that cannot leave your network.
Ways to hire LlamaIndex developers
| Option | Best for | Trade-offs |
|---|
| Freelance marketplace | A quick RAG demo on clean documents | Demos on tidy PDFs hide the parsing problems that show up on real files. |
| Staffing or recruiting agency | Sourcing generalist AI engineers | Agencies rarely test document parsing or retrieval evaluation. |
| In-house recruiting | A permanent document AI team | Long cycles, and the specific skill set is uncommon. |
| Ryz Labs staff augmentation | Adding document AI depth to an existing team | You set direction and review the work. Best when the documents and use case are defined. |
| Ryz Labs AI pod team | A full document processing system built in your cloud | A dedicated pod with a tech lead, ML and backend engineers and weekly demos. Scoped up front. |
We are not the right fit if you want a ready-made document AI product instead of engineers, or hourly work booked without a conversation. Teams that need European or Asian hours should look at global networks.
Why hire LlamaIndex developers from Latin America
Document AI improves through examples. The analyst says "this one is wrong", the engineer looks at the parse, and both agree on the fix. Doing that live, in the same hours as your operations or compliance team, turns a week of back-and-forth into an afternoon.
Latin American engineers have built data and backend systems for US companies for decades, and many have worked with US document formats and processes. Our pod teams have applied the same skills in production, including marketing compliance review across more than 8,000 documents for a global capital management firm. See our case studies.
Related roles
FAQ
LlamaIndex or LangChain?
Both can build RAG and agents. LlamaIndex tends to fit document-heavy work with complex parsing and indexing; LangGraph tends to fit stateful, multi-step agent control flow. Some systems use both. Our engineers pick based on your use case.
Can documents stay inside our network?
Yes. Engineers can use open-source parsers, self-hosted vector stores and models in your cloud account when documents cannot go to an outside service.
What does it cost?
Every engagement is a custom quote, scoped per team. You see a plan, a price and the names of the engineers before signing.
What time zones do they work in?
They work within an hour of US time zones, including New York hours. Ryz engineers work on your team and report to your leads. Talk to us to scope your team.
Questions we didn't answer? Email info@ryzlabs.com.