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 — automated response (kill a malicious process / disable an account on detection) and triage routing by criticality.
- Stand up a quick‐win PoC to anchor the engagement — e.g. an automated dependency / PR vulnerability‐scanning pass, or a head‐to‐head local‐vs‐frontier benchmark of the exploitation agent.
- Turn findings into a defensible technical proposal and roadmap; present methodology and trade‐offs to a technical CISO / CTO audience.
- Keep all sensitive work build‐time and in‐perimeter — no pushing intellectual property, configs, or recon‐enabling data to external model providers; respect regulated‐gaming certification constraints (no uncertified AI in runtime‐critical paths).

Skills (hands‐on First)
- Hands‐on offensive security: vulnerability research, exploit development and chaining, web + network penetration testing; fluent with Nmap, Nuclei, Katana, Acunetix, Metasploit, Burp Suite and Kali tooling.
- Building and operating LLM agents for security work — agentic tool‐use, sandbox orchestration, prompt / flow design for recon and exploitation, guardrails for autonomous exploitation.
- Local / self‐hosted open models: running and tuning open weights (Kimi, GPT‐OSS, MiniMax, DeepSeek) on rented or private GPU; quantization, throughput and the agentic‐performance trade‐offs that matter for security automation.




- Exploit & threat intelligence: sourcing and validating exploits (including from underground / forum sources), CVE triage, exploitability and severity assessment.
- Runtime detection: designing intrusion / privilege‐escalation pattern detection, anomaly detection, and automated response.
- Cloud security (AWS preferred): sandboxing, container isolation, secure inference hosting.
- Writes their own code (Python + shell) and can explain methodology to non‐security executives.

Knowledge
- Modern offensive‐security methodology and the current exploit / zero‐day landscape.
- Strengths and limits of frontier vs. local LLMs for security automation (agentic tool‐use, reasoning depth, cost‐per‐task).
- Data‐egress / sovereignty constraints: why IP and recon‐enabling data must stay in‐perimeter; private‐cloud (AWS Bedrock) vs. rented‐hardware trade‐offs.
- iGaming / regulated‐infrastructure context and certification constraints (build‐time vs. run‐time AI) — strong plus.
- Defensive side — SIEM, anomaly detection, incident response — plus.

Experience

Key characteristics (ideally 4/4)

- Hands‐on offensive security
- Built or operated AI / LLM‐driven security automation (agents, pipelines), not just used a chatbot
- Cloud hyperscaler experience (AWS preferred)
- Technology consulting / client‐facing delivery — can lead a CISO‐level technical conversation

Role‐specific characteristics
- 3+ years hands‐on offensive security / vulnerability research / red‐team
- Demonstrable exploit development and chaining; comfortable with zero‐day research and exploit intelligence
- Has wired LLMs into real security workflows (recon, exploitation, triage)
- Has run self‐hosted / local open models in a real engagement, with a view on cost and hardware
- Comfortable being the sole domain expert in the room and owning the methodology

Terms & conditions
- Allocation: ~0.25 – 0.5 FTE initially (discovery/advisory + joint CISO sessions), scaling with the engagement

📌 AI Architect (AI for Security) (Madrid)
🏢 Neurons Lab
📍 Madrid