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Salary
≈ $40k – $112k per year (Estimated)
Location
In office (Singapore)
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 12, 2026. First seen by Alion on Aug 18, 2026.

Overview
Company
Impact
Profile match
Deploy a fully managed global recruiting team with elastic monthly capacity, unlimited hiring, and no placement fees.

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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