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)
Aedas provides opportunities for long term career development with an expanding international practice. We offer attractive compensation and benefit packages. Interested parties please send detailed CV, current & expected salary and availability by email.
Aedas is an Equal Opportunity Employer
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