Senior AI Engineer — LLM Agents

About the role

Patsnap's Materials team builds AI systems that help R&D scientists and engineers search extract and reason over materials science and patent data. You will own the agentic layer of our products end-to-end LLM-powered agents tools (MCPs) and the evaluation frameworks that prove they beat general-purpose AI for our customers. You will be the AI engineer for this team — sole owner of the agentic stack working directly with product managers materials domain experts and our platform team. ➡

What you will do

➡ Design build and productionize agentic systems (multi-step reasoning tool orchestration guardrails) for materials science search Q&A and information extraction. Develop integrate and maintain memory systems MCP servers and agent skills in a multi-agent environment. Build evaluation frameworks with domain experts to measure answer quality extraction accuracy and retrieval performance. Own production reliability & observability of agents you develop. Advise adjacent teams on agentic and search system design flag technical risk and feasibility during roadmap planning.

Requirements

➡ Degree in engineering computer science or a quantitative/physical science — or equivalent practical experience. 5+ years of software/ML engineering including 2+ years building LLM-based systems that run in production. You have designed evaluations for LLM/agent systems — eval sets quality metrics human-expert or LLM-judge pipelines — and can walk us through one (e.g. promptfoo Braintrust LangSmith DeepEval or your own harness). You have instrumented monitored and debugged live AI services (e.g. OpenTelemetry Arize Phoenix Langfuse Datadog or similar). Strong Python able to independently build and deploy services. Strong pluses (not required — you'll have room and support to pick these up on the job) ➡ Search/RAG vector databases keyword search knowledge graphs reranking hybrid retrieval MCP (Model Context Protocol) or agent-tool ecosystem experience Materials science chemistry or patent/IP domain exposure Structured information extraction from technical documents (tables compositions specs) Why this role ➡ Full ownership of a production agent stack that customers pay for Your evals help decide the roadmap we build where we can measurably beat frontier general agents Small senior team direct access to domain experts and real R&D users ➡