Responsibilities:

• Manage the implementation of AI use cases within the risk management function, identifying and assessing opportunities for AI application in specific business scenarios, and driving the execution of solutions.
• Deliver the full AI use case lifecycle for identified use cases-from data preparation and model training to testing, deployment, monitoring, and iterative improvement.
• Conduct AI solution selection by evaluating and choosing appropriate AI tools, large language models (LLMs), or frameworks (e.g., PyTorch, TensorFlow) based on business needs, and perform fine-tuning or RAG optimization as required.
• Design and orchestrate AI agent workflows using internal agent platforms, building agents with capabilities in planning, reflection, and tool integration. Implement evaluation-driven development by establishing comprehensive end-to-end evaluation to assess agent performance, ensure reliability, and maintain workflow stability.

Requirements:

• Master or PhD degree in computer science, mathematics, or related fields.
• Minimum of 3 years of professional experience in artificial intelligence technologies, with at least 1 year in core technical roles within AI, data science, or digital transformation. Proven hands-on experience in AI project development and a track record of successfully delivering AI solutions into production environments. Strong foundation in data mining, algorithms, and system architecture. Experience in the banking industry is highly preferred.
• Technical expertise in AI: Proficient in Python programming; familiar with ML Ops/LLM Ops practices and experienced in deploying AI services on premise or cloud platforms.
• Knowledge of banking operations, relevant regulatory requirements, and risk management frameworks. Strong communication skills with the ability to collaborate effectively with business experts to translate industry pain points into algorithmic requirements, and to drive cross-team and cross-departmental collaboration.
• Demonstrated ability to troubleshoot complex engineering challenges, including resolving performance bottlenecks, latency issues, and accuracy concerns (e.g., mitigating model hallucinations) in large-scale AI systems.
• Capability to maintain and optimize the AI tool stack: skilled in Agentic Frameworks and performance tuning of vector databases; strong API orchestration skills to enable seamless integration of intelligent agents with legacy banking systems.
• Advanced Prompt and Context Engineering skills: Mastery of few-shot learning, chain-of-thought (CoT) prompting, and agent-specific prompting techniques to address issues such as "agent derailment" or loss of focus during long-horizon tasks.
• Excellent command of written and spoken Chinese and English.

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