Build intelligent applications on top of large language model APIs, covering Agent workflows, intelligent Q&A, automated report generation, and process automation assistants.
Design and implement RAG (Retrieval-Augmented Generation) pipelines, including text chunking strategies, Embedding management, vector store integration, and retrieval quality optimization.
Build AI Agents with tool-calling and function-calling capabilities to orchestrate complex multi-step business workflows such as merchant onboarding review, document verification, and compliance filing.
Write, evaluate, and optimize prompts to ensure stable, accurate, and business-relevant model outputs manage multi-turn conversation state and context memory to maintain coherence and reliability.
Package AI capabilities into reusable, versioned APIs or microservices, following microservices design principles.
Design the overall architecture of AI applications with service-oriented, modular, and multi-tenant characteristics to support the productization of AI capabilities across different business lines.
Build automated evaluation, monitoring, and alerting systems to continuously track model performance (accuracy, latency, cost), and iterate on core metrics such as risk control accuracy and human replacement rate.
Integrate with core backend business systems, complete interface integration and data pipeline setup with payment, risk control, and compliance platforms, ensuring financial-grade security and compliance requirements are met.
Ensure AI services maintain stability, scalability, and fault tolerance in regulated financial production environments.
Work closely with product, operations, and business teams to identify and refine AI implementation opportunities.
What We're Looking For:
Bachelor's degree or above in Computer Science, Artificial Intelligence, or a related field 8+ years of software development experience, with at least 4 years in AI application engineering and production deployment fintech / payments industry background preferred.
Proficiency in Python, Java, Go, or Rust solid hands-on experience with microservices architecture, caching, databases, and message queues.
Strong system architecture design skills - able to design AI applications as service-oriented, modular, multi-tenant, and productizable systems, with thorough consideration of stability, observability, and cost control.
Deep practical knowledge of core LLM application technologies, including Prompt Engineering, RAG architecture design and optimization, Agent framework usage, and Function Calling / Tool Use mechanisms.
Understanding of business logic in cross-border payments, financial risk control, anti-fraud, and compliance review hands-on AI experience in financial / payment contexts is a plus.
Strong problem decomposition skills and cross-functional collaboration abilities capable of independently driving AI project delivery in a fast-paced environment.
