A1
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There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.

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. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.

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

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- 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

Idóneo Experiences

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- 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

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- 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 xqbhyrx 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

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- Next.js

- Python

- NodeJs

- Pytorch

- OpenAI / Anthropic / open-source LLMs

- SQL & noSQL

- Kubernetes

- Docker

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📌 Full Stack Engineer, AI systems (España)
🏢 Bjak
📍 España