Landing Your First RAG Role

You've learned the pipeline and built a harness. Here's how to turn that into a job.

Build a portfolio — this is the whole game for new grads

Take the pipeline you built and point it at a real, public corpus — a documentation set, a Wikipedia slice, or a Hugging Face dataset. Build the full path: chunk, index (dense + BM25), retrieve, rerank, and generate cited answers, with an eval harness that reports recall@k and faithfulness on a small gold set. Put it on GitHub with a short write-up: what you retrieved, where it failed, what you fixed, and the before/after numbers. One real, measured pipeline beats a page of buzzwords — and almost no other new grad will have one. (Priya, in Chapter 12, got hired on exactly this.)

What to search for

Titles vary: RAG Engineer, AI Search Engineer, Applied AI Engineer, Search Relevance Engineer, ML Engineer (Retrieval), Knowledge / Enterprise Search. Also look inside broad "AI Engineer" and "Applied ML" postings — retrieval is often the bulk of the work even when it isn't in the title. Any product described as "chat with your documents" is a RAG job in disguise.

What entry-level interviews reward

Diagnosis over jargon. Handed "our RAG bot gives wrong answers," they want to hear you split the failure into stages — was the answer-bearing chunk even retrieved? — before touching the prompt. Name recall@k, hybrid retrieval, reranking, and grounded-with-citations generation, and always say how you'd measure the change. The Interview Simulation appendix drills exactly this — practice it out loud.

Resume framing

Lead with what you built and measured, not courses taken: "Built a hybrid RAG pipeline (dense + BM25 + cross-encoder rerank) with grounded citations; measured retrieval recall@k and answer faithfulness on a 500-query gold set, lifting recall@5 from 0.71 to 0.93." Numbers and verbs — that one line signals you can do the job.

A two-week plan

Week 1: read Ch 1–6, do every lab. Week 2: read Ch 7–12, do every lab, then build the portfolio pipeline on a real corpus. Rehearse the ten interview questions below. Apply.