Key Responsibilities
Design and implement LLM-based agent architectures, including task planning, dialogue management, intent recognition, and workflow optimization.
Develop and maintain backend services for AI agents, ensuring stability, scalability, and high performance.
Stay on top of cutting-edge AI Agent technologies, continuously driving improvements in architecture and performance.
Collaborate with cross-functional teams—including product, design, and development—to deliver high-quality AI agent solutions on schedule.
Requirements
Educational Background:
Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field. Advanced degrees (Master's or Ph.D.) are highly preferred.
Technical Skills:
Strong hands-on experience with one or more programming languages, such as Python, Go, JavaScript, or TypeScript, with a solid foundation in software architecture and application design.
Deep understanding of AI Agent ecosystems and experience with frameworks such as AutoGPT, LangChain, LlamaIndex, AgentKit, Swarm, etc. Proficiency in Prompt Engineering is highly desirable.
Familiarity with NLP, machine learning, and fine-tuning models is a plus.
Expertise in designing, testing, and optimizing prompts, including advanced techniques like COT (Chain-of-Thought), TOT (Tree-of-Thought), GoT (Graph-of-Thought), and RAP (Reinforcement-Aware Prompting).
Better to have: Experienced in financial investment or trading would be preferred.