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Senior AI Engineer — LLM Agents

patsnap
Singapore
•On-site•
Full-time
•
Posted 8 days ago
SG
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Job Description

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.

Responsibilities

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.

    • 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

    • Requirements

    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.

    • Ready to Apply?

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