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Add developers who can turn private and operational data into measurable LLM applications—designing ingestion, parsing, retrieval, RAG, agents, workflows, evaluation, observability and governed handoff.
16+ Years of AI & Software Delivery
250+ Engineers Across AI, Data, Cloud, Security & Product Engineering
U.S.-Led Project Management | Global Delivery
Hire LlamaIndex developers for a defined ingestion, retrieval, RAG, agent, evaluation or integration gap—or for ownership across data, application, security and operations. This page focuses on LlamaIndex data, RAG and agent talent; for Meta Llama model-family engineering, hire Llama developers. For multi-provider LLM talent, hire LLM engineers, or explore LLM development services for a fully managed project.
Translate the use case into data, permissions, freshness, evidence, latency, scale, risk and ownership requirements; validate the framework and managed-service boundary.
Connect approved sources; parse documents and structured data; define nodes, metadata, transformations, sync behavior, lineage, deletion and re-indexing.
Build vector, lexical or hybrid retrieval, filters, reranking, context assembly, citations, abstention and access-aware answers against a measured baseline. Prefer a fully managed retrieval project instead of embedded talent? Explore RAG system development.
Create bounded agents and workflows with typed inputs, tool permissions, state, checkpoints, human approval, retries, error handling and audit events. For broader agent talent beyond LlamaIndex specialization, hire AI agent developers; for protocol-level tool and server integration, hire MCP developers.
Measure retrieval and response behavior; threat-model data and tools; add tracing, regression gates, access controls, privacy safeguards and incident evidence.
Integrate application APIs and enterprise systems; package services, CI/CD, monitoring, runbooks, cost controls, rollback and ownership transfer. For model platform and lifecycle ownership beyond application-level work, hire MLOps engineers.
Our LlamaIndex developers bring proven experience across ingestion, parsing, retrieval, RAG, agents, workflows, evaluation, security and observability. For broader generative AI application talent beyond LlamaIndex specialization, hire generative AI developers.
Review a representative role profile, then request current CVs matched to your data sources, parsing needs, retrieval design, target LLM, vector/search layer, application stack, deployment environment, evaluation criteria, security controls and working-hour overlap.
Keep each item visibly labeled "Solution Blueprint" until DreamzTech verifies the client, production status, contribution, evidence, outcome and permission to publish.
Environment: enterprise documents
Core: LlamaIndex, hybrid retrieval, reranking, citations
Connect governed sources and answer only from accessible evidence. Accept on ingestion coverage, retrieval recall, citation support, access isolation, abstention, latency and cost.
Environment: operations or compliance
Core: LlamaParse, schemas, validation, human review
Parse complex documents into versioned fields and route low-confidence cases. Accept on field accuracy, format validity, exception coverage, privacy, reviewer effort and auditability.
Environment: analyst or support operations
Core: LlamaIndex agents, workflows, APIs, checkpoints
Coordinate retrieval and approved tools with state, limits and human approval. Accept on task completion, source support, tool correctness, escalation, recoverability, latency and cost.
Simple & Transparent Pricing | Fully Signed NDA | Code Security | Easy Exit Policy
Begin with the user outcome, data sources, permissions, required evidence, sensitive information, traffic, latency and failure tolerance. Share prototypes, retrieval failures, evaluation sets and the infrastructure your team must own.
Share your LlamaIndex use case, data and quality bar and we will design the fastest path to a production system.
Our LlamaIndex developers bring proven experience across ingestion, parsing, retrieval, RAG, agents, workflows, evaluation, security and observability.
| LlamaIndex Framework | PythonTypeScriptcore settingsquery/chat enginesagentsworkflowsapproved packages |
| Parsing & Document Intelligence | LlamaParse where selectedPDFsOffice filesHTMLtablesimagesOCRschemasvalidation |
| Connectors & Ingestion | LlamaHub/approved readersAPIsdatabasesfilesobject storagequeuestransformationsmetadatasync |
| Retrieval & Indexing | Vectorlexicalhybrid and structured retrievalfiltersroutersfusionrerankingknowledge graphs |
| Vector & Search Stores | pgvectorPineconeWeaviateQdrantMilvusElasticsearch/OpenSearchChromaapproved managed stores |
| LLMs & Embeddings | OpenAIAnthropicGeminiBedrockAzureHugging Face/local modelscompatible embedding providers |
| Agents, Tools & Workflows | Typed toolsfunction callingMCP where justifiedstatecheckpointsretrieshuman approvalevent-driven flows |
| Evaluation & Testing | Retrieval measuresresponse supportfaithfulnesstask successlabeled datasetshuman reviewregression suites |
| Security & Governance | Least privilegesource ACLstenant isolationsecretsPII controlsinjection defensesaudit eventsretention |
| Observability & Operations | InstrumentationOpenTelemetry-compatible tracingqualitylatencytoken/costerrorsalertsincidentsrollback |
| Applications, Cloud & Delivery | FastAPINode.jsREST/GraphQLDockerKubernetesCI/CDAWSAzureGoogle Cloudinfrastructure as code |
This is a capability map, not a claim that one developer knows every connector, model, vector store, cloud, evaluator and enterprise system. Match the CV to the source systems, scale, risk and ownership model.
Hire dedicated AI developers for your project with a quick, efficient hiring process. Build your AI engineering capacity faster with matched, evaluated talent.
Tell us the LlamaIndex use case, data sources and stack involved, and the evaluation criteria that define success.
Review matched LlamaIndex developer profiles and interview candidates on ingestion, retrieval, agent, evaluation and security experience.
Confirm scope, access and onboarding readiness—including contracting, data/system access, security review and realistic start timing.
Hire LlamaIndex developers who deliver secure, well-tested, data-grounded systems across a wide range of industries.
Manufacturing
Logistics
Retail
eLearning
Fintech
Agriculture
Travel
Casino
Sports
Healthcare
Real Estate
Facility
Production LlamaIndex work crosses data engineering, LLM application development, security, UX, cloud and operations. DreamzTech can match the core developer and connect adjacent specialists when the use case crosses role boundaries.
Share the use case, sources, permissions, target stack and production goals. We will respond with the likely developer profile, readiness questions and a practical first scope.
Got questions about hiring LlamaIndex developers? Explore the FAQs below to learn how DreamzTech matches LlamaIndex developer talent to your data, retrieval and deployment needs.
LlamaIndex is an open-source framework for building context-augmented LLM applications over your data. A LlamaIndex developer connects sources, designs ingestion and metadata, builds retrieval and response pipelines, implements agents or workflows where justified, integrates applications, evaluates behavior, adds observability and security controls, and documents deployment. LlamaIndex is not an LLM and does not require Meta Llama.
LlamaIndex is especially data-centric: ingestion, indexing, retrieval, query engines and context augmentation are core strengths, alongside agents and event-driven workflows. LangChain offers a broad application integration ecosystem, while LangGraph emphasizes graph-based stateful agent orchestration. They can be combined. Choose from the dominant problem, team experience, evaluation needs, operational complexity and lock-in—not a generic popularity comparison.
Yes, through approved connectors, readers, APIs, databases and custom ingestion code, but connection is only the start. A production implementation must preserve source identities and permissions, define freshness, deletion and re-indexing, isolate tenants, protect secrets and sensitive data, and test that retrieval never crosses access boundaries. Validate each connector and managed service against your residency and security requirements.
Use RAG when the application must answer from current or private evidence. Add an agent when the system must choose among bounded tools, and use a workflow when the task needs explicit multi-step state, events, approvals, retries or recovery. Start with the simplest design that meets the acceptance tests; more autonomy increases evaluation, security and operations work.
Often yes: LlamaIndex documents integrations across many hosted and local LLMs, embeddings, vector stores and cloud services, and it allows custom components. Compatibility still depends on the exact package versions, model features, async and streaming behavior, metadata filters, tenancy, network boundaries, rate limits and deployment target. Prove the intended stack with a thin integration and representative load before committing.
They create labeled questions, expected sources, tool outcomes and failure cases, then measure retrieval coverage, evidence support, answer quality, abstention, task success, latency and cost. They enforce least privilege, source ACLs, tenant isolation, secrets and PII controls, injection defenses, tool limits and human approval. Traces, versioned datasets and regression gates make model, prompt, data, index and code changes reviewable.
Cost depends on seniority, source complexity, parsing, retrieval, agents, integrations, evaluation, security, scale, support and working-hour overlap. DreamzTech may publish $20 per hour or $3,200 for a 160-hour monthly allocation only after sales confirms applicability. LLM and embedding usage, LlamaCloud/LlamaParse plans or credits, vector/search services, storage, cloud, observability, data preparation and extended support are separate unless included by contract.