AI/RAG Engineer

🕒 September 29, 2025

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CoinMarketCap

WebsiteLinkedInAll Job Openings

51 - 200 employees

₿ Crypto

💸 Finance

🛍️ eCommerce

Crypto • Finance • eCommerce

CoinMarketCap is a leading cryptocurrency market data platform that provides real-time information on cryptocurrency prices, trading volumes, market capitalization, and more. It serves as a comprehensive resource for investors and traders to track various cryptocurrencies and analyze market trends.

📋 Description

• Building AI search agents- including ReAct, planning, and multi-agent architectures via custom implementation or frameworks like LangGraph, Dify, or CrewAI. • Building end-to-end RAG pipelines from ingestion, chunking, embeddings, and hybrid vector search, ideally using Opensearch. • Operating and monitoring vector/hybrid indexes (e.g. OpenSearch) in production environments. • Implement grounding and citation to link generated answers back to their exact source passages. • Automate evaluation using synthetic QA, retrieval-hit-rate tracking, and model-critique loops to continuously measure accuracy and detect drift. • Orchestrating external tools or knowledge bases and monitoring latency and cost at production scale.

🎯 Requirements

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. • 3+ years of experience in developing 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 machine learning frameworks (e.g., PyTorch, TensorFlow). • Strong understanding of data ingestion, chunking, embeddings, and hybrid vector search techniques. • Experience with monitoring and managing production environments. • Knowledge of grounding and citation techniques in AI-generated content. • Familiarity with synthetic QA datasets and evaluation metrics.