AI & Machine Learning
Hire LLM & RAG Engineers
Engineers who build retrieval-augmented systems that answer from your own data and admit when they cannot.
Retrieval-augmented generation earns its place where the answer already exists somewhere in your documents, tickets or records, but finding it costs a person twenty minutes. It fails, expensively and publicly, when it is bolted onto a corpus nobody has curated, with no evaluation set and no honest measure of how often it is wrong.
When hiring, the question that separates people quickly is how they measure quality. Ask what their golden set looked like, how they told retrieval failures apart from generation failures, and what the system does when confidence is low. Engineers who have run one of these in production will talk about chunking regrets, index refresh and cost per query. Anyone who only talks about model choice has not yet run one.
What these engineers do
- Retrieval pipelines with pgvector, Qdrant or OpenSearch and hybrid keyword reranking
- Chunking, embedding and index refresh strategies for documents that keep changing
- Evaluation harnesses with golden sets, faithfulness scoring and regression gates in CI
- Tool calling, structured output and agent loops designed to fail safely
- Token cost, latency and caching budgets tracked per feature rather than per invoice
Delivered AI-first
This is the one stack where the technology and the delivery method overlap, and the distinction matters. AI assistance is used to generate prompt scaffolding, retrieval test fixtures, evaluation datasets and adapter code between model providers. It does not decide what counts as a correct answer. Golden sets are written by engineers who understand the domain, and every change to a prompt, retriever or ranking function goes through review and an evaluation run before it ships. The measurable effect is throughput per engineer, not fewer reviews.