Need AI-First Software Engineers for migration or customisation tasks?

An AI-first software engineer builds production software by directing coding agents such as Claude Code, Cursor, GitHub Copilot and Codex, and reviews every change before it merges. An AI-first expert is a professional developer with high-level knowledge of security, architecture, cloud, and other essential development areas.

Our network skews senior, and that matters even more for agents. Once an agent can draft a feature in minutes, typing speed is no longer the constraint. The judgment of what the agent got wrong becomes the bottleneck. In the 2025 Stack Overflow Developer Survey, 66% of developers cited AI output that is "almost right, but not quite" as their top frustration with these tools. The engineer who catches the "almost" before your users do is the hire this page is about - and that is the quality we screen for whenever a profile claims agent experience.

Agent output is a draft until an engineer signs off

The failures in agent-written code are specific. An agent calls a library method that does not exist because a similar one did in another version. It writes tests that assert its own mock instead of the behaviour. It refactors files outside the task because they looked related. Over a long session, it drifts away from the architecture rules it followed an hour earlier. None of this shows up in a demo, and all of it shows up in production.

So we read an AI-first candidate's project history for the work around the agent: who wrote the task specs, who maintained the context files, who ran validation before a merge. A profile that talks only about generating code faster fits prototype work better, and for that we match clients with vibe coders.

What has our AI-first developers shipped for clients?

1. Danylo S., a senior software engineer, joined a B2B2C drone inspection platform that needed modernization and faster delivery at the same time. He refactored Go services, owned a major Angular rewrite of the legacy frontend, and set up the team's AI-assisted development pipeline: agent instructions, task decomposition, context management, automated validation, and a review workflow that kept agent-driven changes reliable.
2. Azay K. led backend architecture for a fintech payment and risk platform built around SEPA Direct Debit flows. Idempotent transaction processing, Redis locking, webhook verification, and chargeback reconciliation are the areas where an agent's plausible guess costs money. He worked with Claude Code and GitHub Copilot inside that discipline while mentoring three to five engineers on the team.

Which stacks do AI-first engineers on our bench work in?

They work across React, Next.js, and Angular on the frontend; Node.js with NestJS, Laravel, Python with FastAPI, .NET, and Go on the backend; and React Native for mobile. We match on stack first and agent fluency second because an agent amplifies what its engineer already knows: a strong Laravel developer using Claude Code ships more high-quality Laravel, while a weaker one ships more code to review. For the same reason, we care less about which agent a candidate prefers than how they configure it for a codebase they did not write.

Tell us your stack and the coding agents your team already uses, and we will shortlist engineers who have shipped production work with both. Once you approve a candidate, they can start within 2 days.