Full Stack Developer (AI)
✨ AI Summary
Cygnify, a Singapore-based company, is hiring a Full Stack Engineer (AI) to build production-grade AI-native product features and agent workflows. The role involves designing end-to-end features across frontend, backend, and AI integrations, with a focus on LLM reliability, streaming, and fallback mechanisms. Tech stack includes Next.js, Python, Node.js, PyTorch, OpenAI/Anthropic LLMs, SQL/noSQL, Kubernetes, and Docker. Requires strong full stack experience, solid system design and API architecture skills, and experience with LLMs or RAG systems; ideal for a senior-level engineer comfortable with ambiguity and owning features from idea to production.
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
About the Company
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