Role Overview
This role will be the technical foundation builder for the company’s AI transformation. You will design and build the company-wide knowledge infrastructure and context layer that powers future AI applications. This is a highly hands-on role requiring strong backend engineering capability, LLM application experience, product sense, and the ability to operate independently in a fast-moving, ambiguous environment.
Key Responsibilities
- Design and build the company-wide AI knowledge infrastructure, including company wiki, internal knowledge base, retrieval layer, and context management system.
- Develop scalable LLM application architecture, including RAG pipelines, vector database integration, prompt workflows, API services, monitoring, and deployment.
- Own the end-to-end technical delivery of internal AI tools, from backend architecture and basic frontend integration to deployment, testing, and monitoring.
- Work closely with business, brand, PR, IR, and leadership stakeholders to translate ambiguous business needs into practical AI systems and technical roadmaps.
- Optimize system performance, including token efficiency, latency, caching strategy, retrieval quality, data architecture, and model inference flow.
- Evaluate and integrate AI coding tools, LLM frameworks, vector databases, and third-party APIs to improve development efficiency and product quality.
- Mentor junior engineers or interns when needed, and help establish technical standards, documentation practices, and reusable engineering workflows.