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Full Stack Developer (AI)

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Onsite

Full Stack Developer (AI)

Cygnify

Full Stack Developer (AI)

Skills

NextDockerNodePythonPyTorchMachine LearningAIKubernetes

2 months ago

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Important information


Contract type:

Permanent

Location:

Singapore

Work mode:

Onsite

Published on:

18 August 2026

What they need


Full Stack Engineer – AI

Role

We are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing how agents operate, fail, recover, and deliver consistent value to users.

Focus

  • Build end-to-end product features across frontend, backend, and AI integrations
  • Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps.
  • Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions
  • Design real-time AI interactions with streaming, partial results, and tight latency constraints
  • Improve system reliability, observability, and fallback mechanisms
  • Collaborate closely with ML, backend, and product teams to ship features end-to-end
  • Continuously iterate based on real usage and failure modes

Ideal Experiences

  • Strong experience in full stack engineering (frontend + backend)
  • Solid understanding of system design and API architecture
  • Experience working with LLMs, RAG systems, or AI-powered applications
  • Ability to handle ambiguity and make pragmatic engineering decisions
  • Strong ownership - able to take features from idea to production
  • Comfort working in fast-moving environments with evolving requirements

Outcomes

  • Own and ship AI-native product features that move beyond chat into persistent, goal-driven workflows
  • Design and deploy agent workflows that reliably complete multi-step tasks across tools and sessions
  • Reduce latency and improve responsiveness of AI interactions while maintaining output quality
  • Build robust fallback and recovery mechanisms for LLM and tool failures in production environments
  • Improve the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoring
  • Establish patterns and abstractions for integrating LLMs, memory, and external tools into scalable product systems
  • Contribute to a product experience where AI feels proactive, consistent, and dependable over time

Tech Stack

  • Next.js
  • Python
  • NodeJs
  • Pytorch
  • OpenAI / Anthropic / open-source LLMs
  • SQl & noSQL
  • Kubernetes
  • Docker

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