Applied AI Engineer

Role Summary

We are hiring an Applied AI Engineer to build production-grade AI-enabled applications for internal users. The focus is on enterprise document intelligence and knowledge search, with strong emphasis on governance, traceability, and reliability.

What You’ll Do

• Build and ship AI-enabled applications end-to-end (prototype → production)

• Design and implement retrieval-based AI workflows (semantic search / RAG / assistants)

• Implement governance controls (access control, data expiry, auditability, citation/traceability)

• Define and run evaluation (offline test sets, quality metrics, failure analysis)

• Collaborate with stakeholders to clarify requirements and iterate toward usable outcomes

• Own reliability basics (logging, monitoring, incident-friendly design, documentation)

Core Requirements

• Strong software engineering skills (Python preferred) with experience shipping production systems

• Hands-on experience with retrieval-based systems (RAG / vector search / hybrid search / reranking) or similar

• Solid system design thinking: data modeling, API design, failure modes, evaluation mindset

• Comfortable working with ambiguous requirements and taking ownership

• Fluent communication in English + Chinese

Nice-to-Haves

• Experience with vector databases or search engines (any of Milvus/Qdrant/pgvector/Elastic etc.)

• MLOps/DevOps basics (Docker, CI/CD; Kubernetes is a plus, not a must)

• Frontend experience (React or similar)

• Experience with AI-assisted coding tools (e.g., Copilot/Cursor/Claude Code)