AI Specialist

Job description

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 Competencies

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.