jobs in Cygnify

全职 AI Engineer 工作, 薪水, Cygnify 公司招聘中 - Ricebowl

AI Engineer

Cygnify

Singapore

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工作地点

  • Singapore

职位描述

岗位职责

Role: AI Engineer

Location: Remote (APAC)


We're looking for an AI Engineer to design, build and ship AI-native systems, from agentic workflows and RAG pipelines to the orchestration layer that ties them together. You'll own the build end to end, working closely with leadership to turn business problems into production-ready AI system.


Key Responsibilities

  • Build agentic workflows and multi-agent systems using LangChain, LangGraph or similar frameworks.
  • Design the orchestration layer that coordinates agents, tools and human checkpoints.
  • Manage state, memory and routing across long-running, multi-step workflows.
  • Build for reliability, with retries, fallbacks and clean failure handling.
  • Build and optimise RAG pipelines, from chunking and embeddings to retrieval and re-ranking.
  • Design the data models and pipelines that power search, retrieval and decision-making.
  • Work with vector databases and structured data stores.
  • Connect AI systems with multi-platforms through APIs and webhooks.
  • Set up evaluation, tracing and monitoring to track accuracy, drift, latency and cost.
  • Build with privacy and security in mind, in line with data protection requirements.


Requirements

  • Degree in Computer Science or a related field.
  • AI, software or backend engineering, with recent hands-on work in agentic AI.
  • Strong Python.
  • Proven experience building agents, agentic workflows, RAG and orchestration systems, with LangChain and/or LangGraph in production.
  • Experience with LLM APIs, including prompt design and tool use.
  • Solid grasp of SQL, data modelling and REST APIs.
  • Comfortable with cloud deployment and CI/CD.
  • Has shipped AI-native systems used by real users, not just prototypes.
  • Clear communicator with a product mindset, comfortable in a fast-moving startup.
  • Ideally with experience in building HRTech and/or GTM tech products.
  • Experience with LLM evaluation and observability tools.
  • Exposure to security frameworks.


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