[Hiring] Senior RAG Engineer @Newcode.ai

Role Description

You'll own retrieval. When someone asks our product a question, something has to find the right passage across large sets of confidential documents, decide it really is the right passage, and hand it to a language model whose answer ends up in real professional advice. That whole path is yours:

• How documents get parsed, chunked and embedded.
• How search combines meaning with exact terms.
• How results get reranked.
• How an agent decides what to read next and when it has read enough.

This is where AI products fail without anyone noticing. Your job is to stop that happening, and to be able to show with numbers that it isn't happening. The role is fully remote. Work from anywhere in the EU.

What You'll Do

• Own the retrieval pipeline end to end: document parsing and chunking, embeddings, indexing, and keeping all of it current as clients add and change material.
• Design how we search: combining semantic and keyword retrieval in Qdrant, fusing ranked lists, filtering on metadata, and reranking.
• Build agentic retrieval: query decomposition, tools a model uses to search and navigate documents, multi-step loops that know when to stop, and cost and latency budgets.
• Build the evaluation layer that tells us any of this is working: golden sets, retrieval metrics, regression tests on realistic client data.
• Ship it as fast, dependable services in Python and FastAPI, with heavy work running as background jobs.
• Take data security and isolation seriously.
• Make retrieval hold up across our clients' languages as well as it does in English.

Qualifications

• At least five years building backend systems that run in production, with at least two years shipping retrieval or RAG systems.
• Experience diagnosing a retrieval regression in production and fixing it.
• Experience building and maintaining a golden set.
• Experience running a vector index in production.
• Strong Python skills.
• Comfortable reasoning about FastAPI or a close equivalent, PostgreSQL, and Redis under load.
• Ownership of end-to-end processes, including maintenance and tech debt.
• Curiosity about vector databases and LLMs.
• Experience working with AI coding assistants.
• Comfortable in a startup that changes direction.

Requirements

• Python: 5+ years
• FastAPI: 2+ years
• Vector databases in production (we run Qdrant): 2+ years.
• Embedding models and metrics for judging retrieval quality.
• PostgreSQL / SQL experience.
• OCR-heavy document ingestion at scale; information-retrieval background (BM25, NDCG, recall@k).

The Hiring Process

• Stage 1: Online coding challenge
• Stage 2: Screening Call
• Stage 3: Technical home assessment
• Stage 4: Whiteboard challenge and presentation of take home assessment
• Stage 5: CEO interview

Benefits

• Fully remote across the EEA, Norway included.
• Be a driver of innovation in one of the most exciting AI ventures.
• Work with talented peers in a collaborative, high-energy team.
• Shape both product and culture as we grow.
• Flexible, English-speaking environment.