Builds and owns end-to-end LLM-powered agentic systems for materials science, focusing on multi-agent tool orchestration, guardrails, and domain-specific evaluation frameworks to outperform general-purpose AI in patent/data retrieval tasks.
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)
