AI & RAG Engineer: Build Scalable Retrieval Pipelines

CoinMarketCap; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

BNB Chain is seeking an experienced AI systems engineer to advance retrieval-augmented generation and AI search agent development. You will architect end-to-end RAG pipelines, optimize vector search using OpenSearch, and implement grounding and citation to anchor answers to source passages.

You will also automate evaluation and monitor latency and costs in production. Ideal candidates have 3+ years building AI systems with RAG focus, strong Python skills, and hands-on experience with frameworks

Qualifications

• Bachelor's or Master's in CS/AI/ML or related field.
• 3+ years building AI systems with a focus on retrieval-augmented generation (RAG).
• Proven track record in building and optimizing end-to-end RAG pipelines.
• Experience with AI search agent development using frameworks like ReAct, LangGraph, Dify, or CrewAI.
• Hands-on experience with OpenSearch or similar vector search technologies.
• Proficiency in Python and relevant ML frameworks (PyTorch, TensorFlow).

Responsibilities

• Build AI search agents, including planning and multi-agent architectures, via custom or frameworks like LangGraph, Dify, or CrewAI.
• Create end-to-end RAG pipelines from ingestion to hybrid vector search, ideally using OpenSearch.
• Operate and monitor vector/hybrid indexes (OpenSearch) in production.
• Implement grounding and citation linking to source passages.
• Automate evaluation with synthetic QA, retrieval-hit-rate tracking, and model-critique loops.
• Coordinate external tools or knowledge bases, monitoring latency and cost at scale.

Skills

RAG development

Python programming

ML frameworks

AI systems design

Education

Bachelor's or Master's in CS/AI/ML

Tools

OpenSearch

ReAct

LangGraph

Dify

CrewAI

Job description

BNB Chain is seeking an experienced AI systems engineer to advance retrieval-augmented generation and AI search agent development. You will architect end-to-end RAG pipelines, optimize vector search using OpenSearch, and implement grounding and citation to anchor answers to source passages.

You will also automate evaluation and monitor latency and costs in production. Ideal candidates have 3+ years building AI systems with RAG focus, strong Python skills, and hands-on experience with frameworks