An LLM engineer's job starts where the API call ends: retrieval design, prompt evaluation, cost control, and knowing when fine-tuning actually beats better context. We staff engineers who've done this in production, not just in a notebook.
Most "AI isn't accurate" complaints are retrieval problems, not model problems — we fix the pipeline, not just the prompt.
Every design decision weighs token cost and response time against accuracy — not just "does it work in the demo."
We default to retrieval and prompt engineering first, and only recommend fine-tuning when the data and use case justify it.
Logging, evaluation, and fallback behavior so failures are visible and gradual, not silent and total.
Chunking, embedding, and retrieval strategy tuned to your actual documents.
Multi-step agents that call tools and APIs reliably, with error recovery.
Testing answer quality on real questions, not just spot-checking a few examples.
Training on your proprietary data when retrieval alone isn't enough.
An ML engineer often trains models from scratch. An LLM engineer works with existing foundation models — retrieval, prompting, fine-tuning, and orchestration — which is what most production AI products actually need.
The pipeline that lets an LLM answer using your specific data: document processing, chunking, embeddings, a vector store, and retrieval logic that feeds relevant context into the model.
It depends on scope and duration. We scope a fixed deliverable — a working RAG pipeline or agent system — rather than quoting an open-ended hourly rate.
Yes, when it's the right tool. We evaluate whether better retrieval solves the problem first, since fine-tuning adds cost and maintenance that isn't always necessary.
Tell us what your RAG or agent system needs to do — we'll scope the engineering work and a fixed estimate.