Senior AI Engineer - LLM Agents
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.
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This is an in-office position based in our Singapore office.
Who are we?
Patsnap is a global, pre-IPO company that transforms the way organizations harness their Intellectual Property and Research & Development productivity. Our platform revolutionizes how IP and R&D teams collaborate across the entire innovation lifecycle, using domain-specific AI to accelerate the creation of market-ready products. With over 12,000 customers worldwide, including some of the biggest names in innovation, Patsnap is at the forefront of technological advancement. Our $300M Series E funding round brings our valuation to a $1 billion unicorn status, and we still have a remarkable amount of growth ahead.
We have a vibrant and diverse team with offices in Singapore, Toronto, London, Shanghai and remote teams based in US. Our hyper-growth trajectory is powered by our people, and we are extremely proud of our company-wide vision, work ethic, and entrepreneurial spirit. We are committed to fostering an inclusive environment where talent thrives and ideas bloom.
What You'll Be Doing:
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Design, build, and productionize agentic systems (multi-step reasoning, tool orchestration, guardrails) for materials science search, Q&A and information extraction.
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Develop, integrate and maintain memory systems, MCP servers and agent skills in a multi-agent environment.
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Build evaluation frameworks with domain experts to measure answer quality, extraction accuracy, and retrieval performance.
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Own production reliability & observability of agents you develop.
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Advise adjacent teams on agentic and search system design; flag technical risk and feasibility during roadmap planning.
Why This Role:
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Full ownership of a production agent stack that customers pay for
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Your evals help decide the roadmap: we build where we can measurably beat frontier general agents
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Small senior team, direct access to domain experts and real R&D users
Qualifications:
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Degree in engineering, computer science, or a quantitative/physical science — or equivalent practical experience.
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5+ years of software/ML engineering, including 2+ years building LLM-based systems that run in production.
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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).
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You have instrumented, monitored, and debugged live AI services (e.g., OpenTelemetry, Arize Phoenix, Langfuse, Datadog, or similar).
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Strong Python; able to independently build and deploy services.
Nice-to-haves (not required - you’ll have room and support to pick these up on the job:
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Search/RAG: vector databases, keyword search, knowledge graphs, reranking, hybrid retrieval
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MCP (Model Context Protocol) or agent-tool ecosystem experience
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Materials science, chemistry, or patent/IP domain exposure
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Structured information extraction from technical documents (tables, compositions, specs)
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