Hire RAG Developers Who Ground Every AI Answer in Your Own Data
Hire RAG developers from Entrans to build retrieval-augmented generation (RAG) systems that answer from your documents, databases, and apps. Every answer comes with a source your team can check. Our RAG architects plan the full pipeline, and vetted talent joins your team within 48 to 72 hours.
Why Market Leaders Choose Entrans RAG Developers
Most RAG demos work on ten clean PDFs and break on real company data. Our RAG developers build for the messy version: scanned files, complex tables, outdated pages, and strict access rules.
1. Production RAG From the Thunai Team
We built Thunai, an enterprise AI platform that answers from each customer's own data in over 150 languages. That production work shapes every retrieval pipeline we build for clients.
2. Retrieval Tuned for Accuracy
Our developers combine keyword and vector search, add reranking and metadata filters, and rewrite vague queries. We check that the right passages come back before we tune prompts. If retrieval misses, no prompt can fix the answer.
3. Permission-Aware by Design
Your RAG system respects the same access rules as SharePoint, Confluence, and Google Drive, so users never retrieve files they cannot open. For regulated data, our cybersecurity and compliance team adds audit and privacy controls.
4. Tested Before It Ships
We build a set of real questions with known answers and score every release against it. A change that lowers quality does not reach production.
5. Developers, Architects, and Chatbot Builders
Hire a RAG developer, a RAG architect to design the system, or a RAG chatbot developer for a customer-facing assistant. Talent onboards within 48 to 72 hours.
Hire RAG Developers From Entrans That Are Certified and Experienced
Our RAG developers have shipped retrieval systems for search, support, and document-heavy workflows. Here is the work they own day to day.
Document Ingestion and Parsing
We pull content from PDFs, scans, slides, Confluence, SharePoint, and databases with OCR and structure-aware parsing. Our data engineers set up incremental syncs, so indexes stay current without full rebuilds.
Chunking, Embeddings, and Indexing
Our developers test chunking methods and embedding models against your content, then index the results in Pinecone, Weaviate, Qdrant, or pgvector. Each chunk keeps its metadata for filtering and citations.
Hybrid Search and Reranking
We blend BM25 keyword search with vector search, then rerank results with cross-encoder models. The right passage reaches the model, even when users phrase questions in unexpected ways.
RAG Chatbots and Assistants
Our RAG chatbot developers build support bots, internal copilots, and research assistants that cite their sources. Each bot admits when the answer is not in your data. For voice channels, our conversational AI developers extend the same retrieval layer.
GraphRAG and Agentic RAG
For questions that span many records, we add knowledge graphs with Neo4j or Microsoft GraphRAG. Agents pick the right source, call tools, and check their own answers, backed by our agentic AI framework integration practice.
Evaluation, Monitoring, and Cost Control
We track faithfulness, retrieval recall, latency, and cost per query with Ragas, LangSmith, and Arize Phoenix. Semantic caching and smart model routing keep monthly LLM bills in check.
Schedule Interviews With RAG Developers and Onboard Them Within 48-72 Hours
RAG Development Technology Expertise
LLMs + Frameworks
Vector + Search
Ingestion + Embeddings
Evaluation + Ops
Our Customer Success Stories
Built an AI Search Chatbot for Faster Hospital Data Retrieval
Technical Stack: AI Chatbot With NLU, Semantic and Contextual Search, Fuzzy Matching, EHR, HIS, Pharmacy, and Document Management Integration, Result Summarization
Hospital staff struggled to find data spread across separate databases and document systems. Filter-based search did not fit the way doctors, nurses, pharmacists, and admins actually ask questions.
We built a chatbot that takes plain-language questions and searches EHR, HIS, pharmacy, and document systems at once. Semantic search and fuzzy matching handle medical terms and typos, and results come back as short summaries. The hospital gained shorter search times and results tailored to each role, which supports faster decisions on patient care.
Accelerated Prior Authorization With LLM Document Extraction
Technical Stack: OCR, Large Language Models, Configurable Rule Engine, Microservices, Automated Testing
Prior authorization requests arrived with incomplete data from many parties. Staff validated each document by hand, so approvals were slow and errors crept in.
We used OCR and large language models to extract and structure data from each document, then checked it against payer rules. A microservices setup replaced the old monolith. The result was 3X faster prior authorization processing, 70% less manual effort, and 95%+ extraction accuracy as volumes grew. This parsing work is the foundation of every reliable RAG pipeline.
Designed for Enterprise Speed and Control
Hiring RAG talent should never put your data at risk. Our five-step process puts vetted developers on your project fast, while you keep control of access and scope.
1. Share Your Requirements
Tell us your sources, such as SharePoint, Confluence, PDFs, or databases. Share your rough document volume, accuracy goals, and who should see what.
2. Get Curated Profiles (Within 24-48 Hours)
Review pre-screened RAG developers and RAG architects who have shipped retrieval systems to production, not just notebooks.
3. Evaluate and Interview
Give candidates a real scenario. Ask how they would chunk a 300-page contract, fix a wrong answer, or enforce document permissions.
4. Onboard and Kickoff (Within 48-72 Hours)
Your developer gets scoped data access and writes a baseline set of test questions in week one. You can then measure every change.
5. Continuous Support and Scaling
Add data engineers, QA, or MLOps support as sources and users grow. We keep indexes fresh and track answer quality over time.
Our Hiring Models
Dedicated RAG Developers
Best for knowledge systems that keep growing. Your developer owns ingestion, retrieval, and tuning as new sources and users come online.
Team Augmentation
Add RAG developers or a RAG architect to your AI or data team. Pair them with our LangChain developers when your stack runs on LangChain or LangGraph.
Project-Based Engagement
Hire for a set scope, such as a RAG proof of concept or a retrieval audit. We also move existing systems to hybrid search. You get a fixed plan and clear deliverables.
Industries Where Our RAG Developers Deliver Impact
We serve clients around the world, with a focus on healthcare, banking, insurance, and manufacturing. Our RAG developers build systems that follow each industry's rules on privacy and audits.
Chief Executive Officer
Chief Executive Officer
Looking to Hire RAG Developers to Build AI Your Team Can Trust?
Book a Free ConsultationFrequently Asked Questions
What does a RAG developer do?
A RAG developer builds systems that let a large language model answer from your own data. The work covers document ingestion, chunking, embeddings, vector and keyword search, reranking, prompt design, and testing. Senior developers also handle access controls, index updates, and monitoring after launch.
What is the difference between a RAG developer and a RAG architect?
A RAG architect designs the whole system: data sources, retrieval strategy, security model, test plan, and cost targets. A RAG developer builds and tunes the pipeline inside that design. Complex or regulated projects usually need both, with the architect often part-time.
How much does it cost to hire a RAG developer?
In the US, mid-level RAG engineers earn about $130,000 to $175,000 a year, based on 2026 recruiter data. Senior RAG engineers earn $195,000 to $290,000. Project budgets commonly run from $15,000 to $300,000 or more, depending on data volume, sources, and security needs. Our hybrid US and India delivery model lowers total cost while keeping senior oversight.
Should we use RAG or fine-tune a model?
Use RAG when answers must reflect current, private, or fast-changing data and need citations. Fine-tuning fits when a model must learn a style, format, or narrow skill. Many enterprise teams start with RAG and add light fine-tuning later only if needed.
How do RAG developers keep answers accurate and secure?
They test retrieval and answers against real questions and track quality on every change. Retrieval follows the same document permissions users have in the source systems, so no one sees files they cannot open. Developers also add PII redaction, audit logs, and clear refusals for questions the data cannot answer.
