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

The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product

Interview process

If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.

Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.

We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.