Head of AI / Head of Applied AI

Key Responsibilities

1. Responsible for the planning and implementation of the product's AI direction. Build AI-native product capabilities around market interpretation, signal extraction, information summarization, alarm intelligence, and research workflow automation.
2. Lead the design and implementation of solutions such as LLM, RAG / Retrieval, Tool Calling, and Agent Workflow in business, and integrate with multi-source data such as market, on-chain, news, and user behavior.
3. Collaborate with product, design, data, and engineering teams to promote AI functions from requirement definition, prototype verification to production launch, and continuously improve user value and adoption rate.
4. Establish an evaluation, monitoring, and iteration mechanism for AI functions, balancing effectiveness, stability, latency, cost, and controllability.
5. Participate in key system design, technology selection, and core module development in a hands-on manner.

^{Job Requirements}

1. More than 3 years of experience in software R&D or AI application R&D, with experience in the implementation of AI products from 0 to 1 or from 1 to N. Have core work experience in well-known frontline AI giants or unicorn enterprises (such as ByteDance, Kuaishou, Manus, OpenAI, Anthropic, ByteDance AI Lab, Moonshot, Baichuan, etc.) or leading financial AI departments.
2. Familiar with mainstream AI application paradigms such as LLM applications, Prompt / Workflow design, RAG, Agent, Tool Use, and Structured Output.
3. Have experience in evaluation, monitoring, and optimization in real production environments, and understand that AI implementation is not just a demo, but a business system.
4. Possess solid software engineering capabilities.
5. Have strong product understanding and business abstraction capabilities, and be able to promote AI implementation from the perspective of business value.
6. Have good Chinese and English communication skills; bachelor's degree or above; experience in trading, fintech, or Crypto is a plus.

Preferred Qualifications

1. Experience in building AI evaluation systems, monitoring systems, quality thresholds, or rollback mechanisms.
2. Have player-coach type technical leadership experience, being able to work in the field and lead the team to establish standards.
Graduates majoring in AI from well-known universities can apply for this position with half a year of AI product internship experience.