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Freelance RAG Engineer

I build retrieval systems that are actually usable in production: grounded answers, measurable quality, and clear failure modes.

Where I create leverage

RAG architecture review before teams waste months on the wrong stack
Chunking and retrieval strategy tuned for enterprise documents and operational knowledge
Offline evaluation, regression alerting, and observability so quality stays stable after launch

Typical deliverables

Retrieval architecture blueprint
Chunking and indexing strategy
Evaluation dataset and quality scorecard
Runtime observability and regression checks
Production rollout plan with fallback behavior

Best fit

Internal copilots on policy, finance, support, or operations knowledge
Teams with a prototype that answers well in demos but breaks in real usage
Companies that need stronger groundedness before wider rollout

Expected outcomes

Higher answer relevance and groundedness

Fewer hallucinations and escalation loops

Clear release criteria for every retrieval change

FAQ

When do you intervene on a RAG project?Best case: before the stack is frozen. In practice I often step in when a prototype already exists but quality is unstable or impossible to measure.

Do you only work on the model side?No. The real leverage is system-level: retrieval, data prep, guardrails, evaluation, tracing, and deployment discipline.

Want to scope this properly?

Send me the use case, current stack, and where the system is failing today. I can usually tell quickly whether the leverage is architecture, evaluation, hardening, or delivery.

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