Hire RAG Engineers
Hire RAG engineers to build AI applications that retrieve relevant information from approved data sources before generating context-aware responses. Add pre-vetted Retrieval-Augmented Generation developers to your team for enterprise search, knowledge assistants, document intelligence, and grounded AI systems. Review matched profiles and begin onboarding within 24 hours.
Hire RAG EngineersRAG Development Services We Provide
Our RAG engineers connect language models with your documents, databases, applications, and enterprise knowledge. From data preparation and retrieval architecture to evaluation and deployment, we deliver RAG development services aligned with your business context, security requirements, and expected response quality.
RAG Consulting and Architecture
Our consultants assess your use case, knowledge sources, user roles, infrastructure, and response requirements before defining the RAG architecture. We recommend suitable models, embedding methods, retrieval strategies, vector databases, access controls, evaluation criteria, and deployment environments.
Enterprise Data Ingestion
Build ingestion pipelines that collect information from documents, websites, databases, cloud storage, CRMs, ERPs, and knowledge platforms. Our engineers clean, structure, tag, and prepare content so it can be indexed and retrieved efficiently by the RAG application.
Document Chunking and Embeddings
Transform large documents into searchable units using chunking strategies suited to their structure and meaning. We generate vector embeddings, preserve metadata and document relationships, and prepare content for accurate semantic retrieval across enterprise knowledge collections.
Vector Database Integration
Integrate Pinecone, Weaviate, Milvus, Chroma, FAISS, pgvector, or another suitable vector technology into your RAG system. Our engineers configure indexing, metadata filtering, storage, retrieval, permissions, and scaling according to your data volume and query patterns.
Semantic and Hybrid Search
Combine semantic retrieval with keyword-based search to improve coverage across different query types. We implement query rewriting, metadata filtering, relevance scoring, reranking, and contextual retrieval to identify the most useful information before sending it to the language model.
Custom RAG Application Development
Hire RAG developers to build enterprise knowledge assistants, document-search platforms, customer-support tools, research applications, and internal AI copilots. Each solution is tailored to your users, workflows, knowledge sources, integrations, permissions, and response requirements.
RAG Integration
Connect RAG capabilities with websites, mobile apps, SaaS products, CRMs, ERPs, help desks, and internal systems. Our developers implement secure APIs, authentication, user-level permissions, structured outputs, source references, and connections with approved business data.
RAG Evaluation and Testing
Evaluate retrieval quality and generated responses against criteria relevant to the use case. Testing can cover retrieval precision, answer relevance, faithfulness to sources, citation accuracy, latency, access control, and failure handling through automated checks and structured human review.
RAG Optimization and Maintenance
Improve retrieval accuracy, response speed, scalability, and operating cost after deployment. Our team monitors queries, retrieval failures, outdated content, indexing issues, model behaviour, and user feedback while refining chunking, prompts, embeddings, reranking, and infrastructure.
Explore Our RAG Development Success Story
See how our RAG engineers transform enterprise knowledge into accurate, context-aware, and scalable AI applications.
AI Product Details – An AI-Powered Shopify SEO Automation App
AI Product Details is a Shopify app that helps merchants automate SEO-optimized product titles, descriptions, tags, and meta fields using advanced AI models. Designed for stores with large inventories, it enables fast, scalable, and customizable content creation.- Automatically generate SEO-friendly content in bulk with webhook support.
- Customize tone, language, and formality to match your brand voice.
Hiregroww: An AI-Powered Skill-Based Hiring Platform
HireGroww is an intelligent recruitment and pre-employment assessment platform that helps companies streamline hiring through AI-powered screening, testing, and candidate evaluation. The platform simplifies talent acquisition with automated assessments, resume parsing, skill analytics, and bias-reduction workflows.- Screen resumes, conduct AI-driven assessments, and shortlist qualified candidates faster with intelligent evaluation workflows.
- Track hiring pipelines, monitor candidate performance, and improve recruitment decisions with real-time analytics and automation.
HireBeep - An AI-Driven Talent Acquisition Platform
HireBeep is an online recruitment software that helps businesses streamline hiring by automating candidate screening, job posting, and applicant tracking. The platform uses AI to identify top talent quickly and reduces time-to-hire.- Post jobs across multiple sites and manage applicants in one place.
- Track progress, collaborate with your team, and make data-driven hiring decisions.
CalCounts – An AI-Powered Calorie Tracking App
CalCounts is a smart nutrition and fitness app that helps users achieve health goals by tracking meals, macros, and calories using AI. The app simplifies healthy eating with barcode scanning, food photo analysis, and goal-based meal planning.- Scan food, barcodes or upload meal photos to get instant calorie and macro breakdowns.
- Track progress, plan meals, and stay on top of your fitness goals with AI-powered insights.
Summrised - AI-Powered Book Summary and Podcast App
Summrised is an AI-driven content platform that transforms books into engaging summaries and podcasts using GPT-4 and text-to-speech technology. It helps creators and readers consume knowledge faster through automated insights and audio storytelling.- Extract key themes and generate structured summaries.
- Convert books into full podcast episodes with TTS.
Technology Stack of Our RAG Engineers
Our RAG engineers use proven language models, retrieval frameworks, vector databases, cloud platforms, and evaluation tools to build secure and scalable knowledge-based AI applications.
Programming Languages
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Python -
JavaScript -
TypeScript -
Java -
SQLite
Commercial Language Models
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GPT-4 -
Anthropic/Claude -
Gemini -
Cohere
Open-Source Language Models
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Llama -
Mistral -
Gemma -
Qwen -
DeepSeek
RAG Frameworks
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LangChain -
LlamaIndex -
LangGraph -
Semantic Kernel -
Haystack
Embedding Models
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OpenAI GPT -
Cohere
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Sentence Transformers -
BGE
Vector Databases
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Pinecone -
Weaviate -
Milvus -
Chroma -
FAISS -
pgvector
Search Technologies
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ElasticSearch -
OpenSearch
Data Sources and Storage
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PostgreSQL -
MongoDB -
Redis -
SharePoint Online -
Amazon S3 -
Google Drive
Cloud Platforms
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AWS -
Microsoft Azure -
Google Cloud
Deployment and Monitoring
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Docker -
Kubernetes -
MLflow -
Github -
Jenkins -
LangSmith
Flexible Hiring Models for RAG Engineers
Choose an engagement model based on your RAG project scope, data environment, integration requirements, delivery timeline, and need for ongoing knowledge-base management.
Dedicated Developers
For long-term projects, hire dedicated developers who work as an extension of your team. This model gives you full control, scalability, and consistent delivery aligned with your business goals.
- Transparent monthly pricing
- Full-time or part-time engagement
- Direct communication & control
- Quick onboarding in 24 hours
Time & Material
Perfect for evolving projects where requirements change frequently. Pay only for the time and resources utilized, ensuring flexibility and cost efficiency.
- Pay-as-you-go model
- Flexible scope & iterations
- Transparent hourly billing
- Ideal for MVPs & experimentation
Fixed Cost (Project-Based)
Best suited for clearly defined projects with fixed scope, timeline, and budget. Get predictable outcomes with structured planning and milestone-based execution.
- Fixed budget & timeline
- Clearly defined deliverables
- Milestone-based payments
- Low risk with predictable outcomes
Process to Hire RAG Engineers in 24 Hours
Share your use case, knowledge sources, preferred models, integrations, infrastructure, security requirements, and delivery timeline. We shortlist matched profiles, arrange direct technical interviews, and help you onboard the selected RAG engineer within 24 hours, subject to profile availability and final approval.
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Share Your RAG Requirements
Tell us what you want to build, which information sources are involved, who will use the application, and what integrations, permissions, and response standards are required.
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Review Matched Profiles
We shortlist pre-vetted RAG developers whose retrieval, LLM, data engineering, vector database, and cloud experience aligns with your project.
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Conduct Technical Interviews
Assess shortlisted engineers through technical interviews, coding tests, architecture discussions, or practical RAG scenarios based on your data and application needs.
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Select Your Engineer or Team
Choose one RAG engineer or a dedicated RAG development team based on technical expertise, availability, working hours, engagement model, and budget.
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Complete Onboarding Within 24 Hours
Finalize the engagement, NDA, IP terms, repository access, data permissions, communication tools, and project documentation. Your selected engineer can begin onboarding within 24 hours, subject to availability and approval.
Why Choose Codiant’s RAG Engineers?
Build grounded AI applications with pre-vetted RAG engineers, flexible hiring models, and complete development support.
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1500+
Project launched -
450+
Inhouse Engineers -
19+
Years of Experience -
70%
Repeat Clients & Referrals
Top Mobile App Development Company in USA, UK, Australia (Good Firms)
Best User Experience Company 2023 (Software Suggest)
Best Website Development Company for Online Success 2023 (CyberNews)
Why Hire Dedicated RAG Engineers from Codiant?
A dependable RAG application needs more than a language model and vector database. It requires well-prepared data, accurate retrieval, user-level permissions, source validation, continuous evaluation, and reliable application engineering. Codiant’s RAG engineers bring these capabilities together within a production-ready solution.
Our engineers understand data ingestion, chunking, embeddings, vector search, hybrid retrieval, reranking, prompt design, LLM integration, evaluation, and deployment. We match developers according to the technical and domain requirements of your project.
Get complete support across consulting, data preparation, architecture, retrieval development, model integration, testing, deployment, monitoring, and optimization.
Hire one dedicated RAG developer or assemble a complete team with LLM engineers, data engineers, backend developers, cloud specialists, and QA professionals.
Build RAG applications with role-based access, permission-aware retrieval, secure APIs, audit logs, source controls, and deployment options aligned with your enterprise environment.
Work directly with dedicated or remote RAG engineers through your preferred communication, development, and project-management tools.
Maintain retrieval quality through content updates, index refreshes, query analysis, evaluation, model changes, performance tuning, and infrastructure monitoring.
Frequently Asked Questions
Share your use case, knowledge sources, technology stack, integrations, security requirements, and delivery timeline with Codiant. We shortlist relevant profiles for your review, arrange direct technical interviews, and help you onboard the selected engineer. Final onboarding time is subject to profile availability and your approval.
The cost is based on the engineer’s experience, project duration, data complexity, required integrations, infrastructure, security controls, and engagement model. Codiant offers dedicated monthly, time-and-material, and fixed-cost options. A reliable estimate requires a defined use case, data-source list, feature scope, and delivery timeline.
A RAG engineer should understand Python, LLM APIs, embeddings, vector databases, document chunking, semantic and hybrid search, metadata filtering, reranking, prompt engineering, backend APIs, cloud deployment, and RAG evaluation. They should also know how to implement permissions, source attribution, monitoring, and failure handling.
The timeline is determined by the number and quality of data sources, application features, integrations, security requirements, evaluation standards, and deployment environment. A focused prototype takes less time than a production system with multiple repositories, permission-aware retrieval, enterprise integrations, monitoring, and extensive testing. A project-specific timeline should follow discovery.
Dedicated RAG engineers provide consistent ownership across data preparation, retrieval architecture, development, integration, evaluation, deployment, and optimization. They become familiar with your knowledge sources and business requirements, which supports faster iteration, clearer accountability, and continuous improvement after the application enters production.
