Requirements Summary

AI-native Full-Stack Software Engineer with 5-8 years of experience, strong full-stack development skills, practical experience building with LLMs, understanding of agentic AI concepts, ability to assess AI-generated code critically, and working knowledge of cloud infrastructure and cybersecurity principles

Benefits

Full Job Description

Role Overview We are looking for an AI-native Full-Stack Software Engineer who understands that AI-assisted software development goes far beyond using Cursor, Claude Code, or other coding assistants. You will use and build autonomous agentic development workflows — delegating complex tasks to agents, designing effective context and tool architectures, evaluating outputs, and creating reliable systems in which AI agents can plan, execute, test, and iterate. What You Will Do Design, build, test, deploy, and maintain full-stack product features. Develop AI-native applications and workflows using autonomous agents, LLMs, tools, APIs, retrieval systems, and structured data. Build agentic systems that can plan and execute multi-step tasks with appropriate observability, evaluation, permissions, and human oversight. Why It Might Be a Fit We need someone who can decompose complex engineering objectives into tasks that autonomous agents can plan and execute. You should be able to design the context, tools, permissions, feedback loops, and verification mechanisms agents need to work reliably. Coordinate multiple specialized agents while maintaining architectural and product consistency. Requirements Approximately 5–8 years of professional software engineering experience, or equivalent demonstrated ability Experience owning delivery end to end, not only your own tasks Strong full-stack development skills across modern frontend frameworks, backend development, REST, GraphQL, event-driven, or asynchronous system integrations, relational and/or NoSQL databases Practical experience building with LLMs or agentic AI systems — not only using AI coding assistants Understanding of agentic AI concepts such as tool use and function calling, planning and multi-step execution, agent orchestration and delegation, multi-agent collaboration, memory and context management, retrieval-augmented generation, structured outputs and validation, model selection and routing, evaluations, tracing, and observability, guardrails and permission boundaries, human-in-the-loop workflows Ability to assess AI-generated code critically and validate it through tests, reviews, security checks, and architectural reasoning Working knowledge of cloud infrastructure, containers, CI/CD, production monitoring, and deployment practices Basic understanding of cybersecurity principles, including authentication and authorization, encryption and secrets management, secure API design, dependency and supply-chain security, data privacy and access controls, common web application vulnerabilities Benefits Competitive compensation Fully remote work Equipment allowance Local public holidays Flexible paid time off Direct and regular access to the founders, without intermediaries

Skills and Technologies