Full Stack Developer (AI)

Cygnify
Singapore
On-site

Who this role is best for

Best suited to full stack engineers with AI integration experience working in Singapore's fast-moving tech environment.

Best fit for

  • Candidates with experience building AI-native workflows and deploying them in production
    — “Own and ship AI-native product features that move beyond chat into persistent, goal-driven workflows”
  • Engineers with a track record of designing robust systems and handling failure scenarios
    — “Improve system reliability, observability, and fallback mechanisms”

Things to consider

  • This role may demand a high level of autonomy and ownership in feature development
    — “Strong ownership - able to take features from idea to production”

How to stand out

  • Highlight experience with multi-step agent workflows and real-time AI interactions
    — “Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps”
  • Showcase your ability to integrate LLMs with memory and external tools in production systems
    — “Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions”
  • Emphasize your work with both frontend and backend technologies in full-stack projects
    — “Strong experience in full stack engineering (frontend + backend)”
  • Demonstrate your ability to make pragmatic engineering decisions under ambiguity
    — “Ability to handle ambiguity and make pragmatic engineering decisions”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · IndividualLevel · Mid

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • designing agent workflows
  • reducing latency
  • building robust fallback mechanisms
Typical background
strong experience in full stack engineeringsolid understanding of system design

Skills & requirements

Required

Full Stack EngineeringSystem DesignAPI ArchitectureLlmsReal-time AI Interactions

Preferred

KubernetesDocker

Stack & domain

Full Stack EngineeringSystem DesignAPI ArchitectureLlmsRAG SystemsAi-powered ApplicationsNext.jsPythonNode.jsPyTorchOpenaiAnthropicSQLNoSQLKubernetesDockerProblem SolvingDecision MakingOwnershipTeamworkAdaptabilityCommunicationProject ManagementAICloudInfrastructureSaas

About the role

Original posting from Cygnify via Ashby

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

Source: Cygnify careers (Ashby)

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