AI Architect

About the project (description, duration, stage)

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 exploits, and orchestrates parallel sandboxes safely and reproducibly.

• Optimise cost-per-finding of the existing exploitation pipeline: benchmark local / sovereign open models (Kimi, GPT-OSS, MiniMax, DeepSeek) against frontier models for the recon, exploitation and analysis loops; quantify accuracy / latency / cost trade-offs and recommend hardware sizing.

• Shape the runtime anomaly-detection layer: define which intrusion / privilege-escalation precursor patterns are worth collecting (signal over raw-log volume), and design the missing pieces — (kill a malicious process / disable an account on detection) and by criticality.