AI Agent Engineer

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by over 250 million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.
 
We are building AI Agent systems for core use cases such as AI conversations and AI-powered trading. We are looking for an AI Agent R&D Engineer to develop a conversational agent platform and its underlying capabilities, including tool calling, memory, orchestration, and more. The goal is to transform large language model capabilities into stable, reliable, and continuously evolving product features that can be applied to real business scenarios..

Why Binance
• Shape the future with the world’s leading blockchain ecosystem
• Collaborate with world-class talent in a user-centric global organization with a flat structure
• Tackle unique, fast-paced projects with autonomy in an innovative environment
• Thrive in a results-driven workplace with opportunities for career growth and continuous learning
• Competitive salary and company benefits
• Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)
 
Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success.
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Responsibilities

• Develop an enterprise-grade conversational agent platform to support core business scenarios such as AI search, token insight conversations, trading assistant Q&A, and trading strategy generation.
• Design and implement multi-turn conversation capabilities, including contextual memory, tool calling and orchestration, clarification and follow-up questioning, and fallback handling, to create a natural and coherent conversational experience.
• Build and optimize the large-model application pipeline, including prompt engineering, retrieval-augmented generation (RAG), tool calling / function calling, and workflow orchestration.
• Work closely with product, algorithm, data, and trading teams to deploy conversational AI capabilities into real business scenarios and continuously drive iterative improvements.
• Establish quality monitoring and evaluation systems for production environments, continuously improving key metrics such as first-resolution rate, conversation completion rate, response latency, and user satisfaction.
• Ensure system stability, security, and compliance, while continuously improving performance, availability, and observability under high-concurrency scenarios.

Qualifications

• Master’s degree or above in Computer Science or a related field is preferred.
• More than 2 years of backend development experience, with solid engineering skills in Java or Python and familiarity with common microservices architectures.
• Hands-on experience in deploying large-model / AI applications, with a deep understanding of key technologies such as prompt engineering, RAG, vector retrieval, and function calling.
• Strong complex system design capabilities, with familiarity in caching, message queues, databases, and service governance.
• Strong interest in digital asset trading, market structure, on-chain data, and token-related information, with a willingness to deeply understand trader needs and behaviors.
• Strong data analysis and troubleshooting skills, with the ability to drive continuous system improvement based on logs and metrics.
• Strong cross-functional communication and project execution skills, with a results-oriented mindset.
• Preferred Qualifications
  Experience in AI search, financial information retrieval, or token / project research products.
  Experience with trading products, market analysis, quantitative tools, or strategy platforms.
  Experience building agent platforms, A/B testing frameworks, or evaluation datasets.
  Familiarity with technologies such as Redis, Kafka, Elasticsearch, and vector databases.