AI Architect (AI for Security)

Job description

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