Job Description, Responsibilities & Requirements
About the Position
We are hiring Senior RAG Developers to build accurate AI systems grounded in your data. Join our team for a two-week paid trial.
Where RAG Projects Actually Break
The tutorial works on ten documents. Production breaks on chunking strategy, retrieval quality, stale embeddings, and the lack of any evaluation harness to tell you whether answers are getting better or worse.
That gap between demo and dependable is exactly where most RAG efforts stall - and where our engineers start.
What Our Engineers Own
Ingestion and chunking, embedding and vector storage, retrieval and re-ranking, grounding and citation, and an evaluation loop so quality is measured, not hoped for. Wired into your product, not a notebook.
How It Works
A short readiness audit scopes your data and the retrieval problem. A two-week paid trial confirms fit on real work. If it is not right, you exit on 30 days - no recruiter fees.
Proof
Answers grounded on the client’s own data - accurate enough to beat their built-in report engine.
On Dr. Todd Hall’s platform, our engineer built AI that generates interactive reports grounded strictly in the organization’s own survey and assessment data, not in a model’s guesses. It runs in production and outperforms the platform’s built-in engine across a pipeline that includes a 1,800-student university and a 3,000-staff organization.
Read the case study
Stack
- Python
- Claude (MCP)
- Vector DB
- Node.js
Related Reading
- The New Superpower in Modern Work: Deep Researching with AI
- AI vs Custom Software Development: The 16-Month Reality Investors Ignore
Frequently Asked Questions
Worried your RAG will hallucinate in production?
Start with the Readiness Audit
The Remote Team Readiness Audit evaluates how prepared your team is to bring on a remote engineer. 4 minutes, 10 questions, no email required to see results.
About the Company
Join a team of seasoned engineers dedicated to building reliable and accurate AI systems grounded in your data.