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 actual data, then deploy them to production with the guardrails, monitoring, and integration your business needs.
Start in 48 to 72 hours, no team overhead, no account manager
What kind of system are you building?
Plain answer
A RAG developer designs and builds retrieval-augmented generation systems: pipelines that retrieve relevant information from a knowledge base and use it to ground an LLM's responses. This includes document ingestion and chunking, embedding model selection, vector database setup, retrieval logic, guardrail design, and production deployment. A freelance RAG developer delivers a working, integrated system, not just a proof-of-concept.
Job title decoder
| Title | Primary focus | When you need this role |
|---|---|---|
| RAG developer | Retrieval architecture, knowledge grounding, chatbot/assistant builds | You need an AI system that answers accurately from your specific data |
| LLM engineer | Fine-tuning, prompt engineering, model evaluation | You need to customize model behavior itself, not just what it knows |
| AI engineer | Broader ML/AI system design, may include RAG, training, and MLOps | You need end-to-end AI infrastructure, RAG being one part |
What I offer
Chatbots grounded in your documentation, product catalog, or knowledge base, with escalation logic for anything outside their confidence bounds.
Employee-facing Q&A systems over company wikis, policies, and internal documentation.
Retrieval-grounded Q&A for legal, healthcare, and financial document corpora, with source citation for every answer.
Retrieval combined with multi-step tool use, LangGraph-orchestrated for systems that both answer and act.
Diagnosing and improving underperforming existing RAG systems, from chunking strategy through to guardrail design.
Try the spec-preview widget above, or get it scoped properly.
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, reflects 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, not a single freelance RAG developer, I recommend AtlasML, a dedicated AI/ML engineering team staffing provider. For a single, well-scoped RAG system, working with me directly is typically faster to start and more cost-effective; if your roadmap spans multiple concurrent AI disciplines needing sustained team capacity, AtlasML is built for that scale.
How it gets built
Investment
| Engagement type | What's included |
|---|---|
| RAG proof-of-concept Free | Baseline retrieval pipeline on a sample of your data, evaluation report |
| Production RAG chatbot | Full pipeline, guardrails, deployment, documentation |
| RAG system audit | Diagnose and improve an existing underperforming RAG system |
| Hourly consulting | Architecture review, retrieval strategy design |
RAG and retrieval-grounded systems are core to several delivered projects, including the AI Customer Feedback Classification system and AI Video Summarizer. Full case studies at shreyans.tech/ai-case-studies.
FAQ
