Direct access, no account layer
I am Shreyans Padmani, a freelance RAG developer and AI engineer. I design and build RAG pipelines, chatbots, and knowledge assistants grounded in your business data, then integrate and deploy them with the retrieval, evaluation, guardrails, and monitoring your application needs.
Start in 48 to 72 hours, no team overhead, no account manager
What kind of system are you building?
RAG engineering expertise
A RAG developer designs and builds retrieval-augmented generation systems that retrieve relevant information from your knowledge sources and provide that context to an LLM when generating a response. The work can include document ingestion, chunking, embeddings, vector search, reranking, prompt construction, evaluation, guardrails, API integration, and production deployment.
If you want to understand the underlying concept before deciding whether RAG is right for your project, see What is RAG?
Job title decoder
| Title | Primary focus | When you need this role |
|---|---|---|
| RAG developer | Retrieval architecture, knowledge grounding, chatbot and assistant development | You need an AI system that works with your specific business or organizational data |
| LLM engineer | Model customization, prompting, fine-tuning, evaluation and LLM application development | You need to customize model behavior or build broader LLM capabilities |
| AI engineer | Broader AI system design including ML, LLMs, computer vision, RAG and MLOps | You need broader end-to-end AI engineering rather than a RAG-focused implementation |
What I offer
Chatbots grounded in your documentation, product catalog, knowledge base, or other approved data sources, with fallback and escalation logic for questions outside the available context.
Employee-facing Q&A systems over company wikis, policies, procedures, technical documentation, and internal knowledge bases.
Retrieval-grounded Q&A for legal, healthcare, financial, technical, or business document collections, with source references where supported by the system design.
Retrieval combined with multi-step tool use and workflow orchestration for applications that need to retrieve information and perform defined actions.
Diagnose and improve existing RAG systems, including ingestion, chunking, retrieval quality, reranking, prompting, evaluation, and guardrail design.
Tell me about your data, users, and desired outcome and we can determine the right RAG architecture.
Talk it throughTooling
How we work together
| Factor | Freelance RAG developer (me, directly) | Dedicated team (via AtlasML) |
|---|---|---|
| Best for | A single, scoped RAG system or chatbot build | Ongoing, multi-project AI roadmap needing several engineers |
| Team size | One engineer: me | Multiple senior AI/ML engineers embedded full-time |
| Start time | 48 to 72 hours | Varies by team scope and structure |
| Cost structure | Project-based or hourly, no team overhead | Team-based retainer, reflecting multi-engineer capacity |
Need a full team, not just one developer?
If your business needs a dedicated, multi-person AI/ML engineering team embedded full-time across GenAI, ML, NLP, and computer vision, I recommend AtlasML . For a single, well-scoped RAG system, working with me directly provides a simpler engagement. If your roadmap requires several AI disciplines and sustained team capacity, a dedicated team may be a better fit.
How it gets built
Investment
| Engagement type | What's included |
|---|---|
| RAG proof-of-concept Free | Baseline retrieval pipeline on a sample of your data with an initial evaluation report. |
| Production RAG chatbot | Full retrieval pipeline, guardrails, evaluation, deployment, integration, and documentation. |
| RAG system audit | Diagnosis and improvement of an existing RAG system, including retrieval and evaluation issues. |
| Hourly consulting | Architecture review, retrieval strategy, technical guidance, and implementation planning. |
Pricing depends on data volume, integrations, retrieval complexity, evaluation requirements, and deployment scope. A written technical specification is provided before development begins.
FAQ
