Senior AI Engineer - LLM Agents

About the role

The Materials team at Patsnap creates AI tools for R&D professionals to analyze patent and materials science data. You will serve as the primary engineer for our agentic infrastructure, overseeing the development of LLM-based agents and the evaluation systems that ensure our performance exceeds standard models.

Key facts

What you'll do

• Architect and deploy agent systems capable of multi-step reasoning, tool orchestration, and safety guardrails for scientific Q&A and data extraction.
• Create and manage memory architectures, MCP servers, and specific agent capabilities within a multi-agent framework.
• Collaborate with subject matter experts to design evaluation pipelines that quantify retrieval accuracy and response quality.
• Manage the performance, reliability, and observability of production AI services.
• Consult with internal teams on system design, identifying technical constraints and feasibility during product planning.

Requirements

• Bachelor degree or higher in computer science, engineering, or a quantitative/physical science field, or equivalent professional experience.
• Minimum 5 years of experience in software or machine learning engineering, with at least 2 years specifically focused on production-grade LLM systems.
• Proven experience designing evaluation sets and metrics for LLM agents using tools like Braintrust, LangSmith, promptfoo, DeepEval, or custom frameworks.
• Demonstrated ability to monitor and debug live AI applications using platforms such as Langfuse, Arize Phoenix, OpenTelemetry, or Datadog.
• Proficiency in Python with the ability to independently build and launch services.

Nice to have

• Experience with RAG pipelines, including vector databases, knowledge graphs, hybrid retrieval, and reranking.
• Background in the Model Context Protocol or agent-tool ecosystems.
• Familiarity with chemistry, materials science, or intellectual property data.
• Skills in extracting structured data from complex technical documents like specifications or chemical compositions.

Practical notes

This role offers full ownership of a commercialized agent stack. You will work within a small senior team with direct access to R&D users and domain experts, and your evaluation results will directly influence the product roadmap.