Full Stack Engineer, AI systems

Company

A1 is building a proactive AI smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows.

Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior.

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

How We Work