AI Architect – AI for Security

🕒 June 24

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Neurons Lab

WebsiteLinkedInAll Job Openings

51 - 200 employees

🤖 Artificial Intelligence

🏥 Healthcare

⚡ Energy

💰 Corporate Round on 2022-10

Artificial Intelligence • Healthcare • Energy

Neurons Lab is a globally distributed AI R&D company that helps deep tech innovators to accelerate data-driven products development and launch. Our team has expertise in fundamental sciences, full-stack AI/ML engineering, and product design. Such a rare combination and access to scarce talent allows Neurons Lab to build disruptive solutions for clients in HealthTech and EnergyTech industries.

📋 Description

• 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).

🎯 Requirements

• 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. • 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.

🏖️ Benefits

• Health insurance • Competitive salary • Flexible working hours