Responsibilities:

1. Engage with top-tier industry clients to deeply understand their business scenarios and requirements；

2. Leverage large language model (LLM)-centric thinking to design and implement complex Agent architectures and Workflow systems—covering the full lifecycle from solution design to code implementation；Enable multi-agent AI collaboration and rapidly build and demonstrate prototypes or demos to help clients clearly grasp the value and practical application of AI. Bridge the gap between LLM technology demos and real-world business value creation, driving projects from exploration into formal implementation；

3. Possess strong communication and presentation skills, with the ability to engage directly and frequently on-site with client executives, business users, and technical teams. Focus on rapid prototyping and technical validation, and once the project moves into formal development, act as a critical bridge between the client and internal product/R&D teams to ensure accurate translation and execution of business requirements；

4. Rapid Prototyping & Technical Validation:

（1）Quickly build minimal viable demos or Proof-of-Concept (PoC) prototypes based on client needs (using any effective approach—e.g., low-code tools, simple scripts, or LLM Agent frameworks);

（2）Demonstrate PoC outcomes to clients, clearly articulating the solution logic, application methodology, and expected business value;

（3）Rapidly iterate on demos/PoCs based on client feedback to align stakeholders on requirements and use cases；

5. Project Execution & Communication Bridge:

（1）After the project transitions from PoC to formal development, support product and engineering teams by delivering clear technical specifications, workflow diagrams, and use cases to ensure thorough understanding;

（2）Regularly review project progress and guide the product/engineering team to keep the client’s intelligent application solution on the right technical track.

Qualifications:

1. Bachelor’s degree or higher in Computer Science, Communications, Artificial Intelligence, or a related field (preferred)；

2. Proven experience communicating onsite with clients in both Chinese and English, with the ability to quickly grasp business contexts；

3. Solid understanding of LLM fundamentals, proficiency in evaluating LLM performance and output quality, and familiarity with various post-training techniques (e.g., fine-tuning, RLHF)；

4. Hands-on experience with LLM-related APIs and AI application architectures, including RAG, Agents, and Workflows. Capable of designing and implementing sophisticated multi-Agent collaborative systems；

5. Demonstrated onsite client engagement experience, strong technical solution design capabilities, and the ability to communicate effectively with client technical leadership. Able to rapidly translate business needs into actionable technical solutions or verifiable prototypes；

6. Highly proactive, with a strong sense of ownership and accountability for project success.

Preferred Qualifications：

1. Project experience in AI Agents, enterprise-grade AI applications, or enterprise digital transformation；

2. Background in consulting, vendor-side delivery, or pre-sales solutioning roles；

3. Domain knowledge of banking or telecommunications industries；