About the Position

We are looking for Senior RAG Developers to join our team and build accurate AI systems grounded in your organization's data. Our engineers specialize in creating reliable RAG systems that do not hallucinate and stay accurate as your data grows.

Responsibilities

Design and implement RAG systems that are grounded in your organization's data.
Address challenges such as chunking strategy, retrieval quality, stale embeddings, and evaluation.
Build an evaluation loop to measure and improve the quality of AI-generated answers.
Collaborate with clients to integrate RAG systems into their products.

Requirements

Proficiency in RAG, Python, and Node.js.
Experience with vector databases and major model providers.
Strong understanding of grounding models and evaluation loops.

We Offer

Opportunity to work on cutting-edge AI projects.
A two-week paid trial to confirm fit on real work.
Exit option within 30 days if the trial does not meet expectations.

About the Company

Our team specializes in building reliable RAG systems that outperform built-in report engines. We have successfully implemented AI solutions for organizations with up to 3,000 staff members.

Frequently Asked Questions

Worried your RAG will hallucinate in production?

Our evaluation loop, retrieval re-ranking, and citation mechanisms ensure that quality is measured and improved rather than assumed.

Can you build RAG on our private data?

Yes, we can ground models on your own documents and data with the required data model and access controls.

How do you keep answers accurate?

We use an evaluation loop, retrieval re-ranking, and citation to measure and improve the quality of answers. Guardrails prevent the model from answering beyond its context.

Which vector stores and models do you work with?

We are stack-flexible and work with common vector databases and major model providers, including those matched to your specific constraints.

How do you prove the answers stay accurate as our data grows?

We build an evaluation harness against your real data and questions, so accuracy is measured, not assumed. You see the numbers before scaling, and you own the harness. If the trial does not convince you, you can exit within 30 days.

Senior RAG Developers

Job description