AI Specialist

Responsibilities

- Build, integrate, and iterate AI applications, including agents, RAG solutions, prompt workflows, and AI-enabled business tools.

- Develop Python and SQL-based data pipelines, APIs, and integrations with enterprise systems such as Snowflake, Salesforce, SAP, SharePoint, and approved local platforms.

- Work with LLMs, vector databases, retrieval pipelines, and AI frameworks such as LangChain, LangGraph, MCP, or similar technologies.

- Translate business requirements into working prototypes and evolve them into production-grade solutions with deployment, monitoring, and support considerations.

- Support architecture decisions for China/APAC, including data residency, access control, cybersecurity, cross-border data flow, and approved local AI ecosystems.

- Collaborate with global AI, data engineering, AI Ops, and business teams across regions and time zones.

Basic Qualifications

- Bachelor’s degree or above in Computer Science, Software Engineering, Data Science, or a related technical field.

- Hands-on experience building and shipping at least one LLM application, RAG solution, prompt workflow, or AI agent for real users.

- Strong working proficiency in Python, solid SQL skills, version control, and experience with structured data and unstructured documents.

- Experience with APIs, containerized services, basic delivery pipelines, error handling, and integration with external or enterprise systems.

- Awareness of secure engineering, access control, personal data handling, auditability, and responsible use of AI coding tools.

- Professional fluency in Mandarin Chinese and sufficient English proficiency to collaborate with global teams.

Travel & Work Arrangements/Requirements

Full-time role based in Shanghai, China. Occasional collaboration across global time zones may be required.

Key Competen cies

- Strong builder mindset with the ability to ship fast, learn quickly, and improve solutions based on feedback.

- Good judgment on when to use LLMs versus deterministic, testable code.

- Practical understanding of retrieval quality, embeddings, chunking, source-grounded answers, and evaluation using test cases.

- Comfortable working with non-technical stakeholders and explaining technical trade-offs clearly.

- Curiosity about how AI creates measurable business value in industrial and enterprise environments.