AI Engineer

About the role

Build AI-Native systems and digital employees — production-grade Agent infrastructure, not toy LLM apps.

What you'll do

• Design end-to-end Agent runtime: task decomposition, planning, tool calling, state, memory and error recovery.
• Embed LLM capabilities into business workflows to ship AI-Native applications that execute complex tasks reliably.
• Build the context-engineering and memory layer — long-term memory, short-term memory, retrieval and context trimming.
• Build the tool-calling and execution layer so Agents can drive external systems, APIs and business capabilities.
• Stand up evaluation and feedback loops — automated evals, error analysis, result verification and continuous tuning.
• Design multi-model routing and fallback strategies, balancing quality, cost and reliability.
• Track frontier model capabilities and turn them into production-ready system features.

What we're looking for

• Bachelor's degree or above in CS or related; 3+ years of engineering experience.
• Strong software engineering fundamentals; proficient in at least one of Python, Go or Java.
• Hands-on AI Agent or LLM application experience — you understand Agent mechanics, not just API calls.
• Able to design complex system architecture and end-to-end execution chains independently.
• Systematic grasp of mainstream LLMs and how to choose between them on capability trade-offs.
• Realistic view of model limits; can engineer mitigations — hallucination control, verification, recovery, stability.
• Strong evaluation chops — can design eval systems and continuously improve model performance.
• Good abstraction skills — distill complex logic into reusable modules and platform capabilities.
• Strong communication and ownership; drive complex systems from design to ship.

Nice to have

• Designed complex Agent or multi-Agent systems.
• Shipped AI-Native products from 0→1 or 1→N.
• Familiar with Claude Code, OpenAI tooling, RAG and agent frameworks.
• Built model-eval systems, automated test infra or experimentation platforms.
• Familiar with knowledge bases, permissions, logging and observability for AI systems.
• AI product or AI platform experience.
• 985 / 211 / top-tier overseas university, or experience at leading internet / AI companies.