About The Project (description, Duration, Stage)

Inscríbase sin demora Se espera un gran volumen de solicitantes para el puesto que se detalla a continuación, no espere para enviar su CV.
Hands-on AI-for-Security engagement with a regulated iGaming / online-gaming group. The client's security team is genuinely advanced: they already run an AI-driven offensive-security capability — continuous external-perimeter scanning feeding an LLM agent that plans exploitation, sources and validates exploits, and executes them in sandboxed environments — plus a runtime anomaly-detection layer watching for intrusion and privilege-escalation patterns across their products. They built this themselves and have explicitly asked us to challenge and improve it, not just rubber-stamp it.

This is not a generalist AI project. Neurons Lab brings the AI‑architecture and engagement depth; what's missing is the offensive‑security domain lead who can sit across the table from a hands‑on CISO team as a peer, pressure‑test their pipeline, and own the methodology. You are that expert.



The early work is concrete and consultative: understand what they've built, find where it's wrong or expensive, and propose a better way.

Stage: pre‑engagement / discovery (the immediate next step is a joint technical session with the client's CISO / security engineers). Duration: discovery → advisory / PoC, with strong extension probability as the security program scales across the group.

Reporting: Neurons Lab CTO / engagement lead (@Alex Honchar); partners with the Neurons Lab AI Architect on the account. You are the security domain owner for this track.

What You’ll Actually Do (example Tasks)

-
- Join joint working sessions with the client's hands‑on security engineers; challenge and harden their AI‑driven offensive pipeline end‑to‑end (recon → verification → AI‑planned exploitation → sandboxed execution).
- Design and refine the exploitation agent: how the LLM plans attack paths, selects and validates expl

📌 AI Architect (Madrid)
🏢 Neurons Lab
📍 Madrid