---
title: 'AI Architect (AI for Security) at Neurons Lab'
canonical: 'https://feeny.ai/job/ai-architect-ai-for-security-neurons-lab-romania-nwxt4ta8w6tt'
type: 'job'
last_seen: '2026-09-15'
---

# AI Architect (AI for Security) at Neurons Lab

- **Company:** Neurons Lab
- **Location:** Romania
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-09-11
- **Last confirmed live:** 2026-09-15
- **Apply:** https://jobs.ashbyhq.com/neurons-lab.com/36fcd40e-c840-4617-b580-efacd8d6946f

## 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

## About Neurons Lab

## Company Overview
- **One-liner**: Neurons Lab is an AI engineering partner that helps Financial Services organizations adopt AI and build production-grade agentic systems, from initial use case definition through deployment and continuous delivery.
- **Entity Type**: Private (Bootstrapped / Self-funded boutique firm)
- **Headquarters**: London, United Kingdom (International House, 64 Nile Str, London, N1 7SR)
- **Founded**: 2019
- **Founders**: Igor Sydorenko (CEO & Co-Founder) and Alex Honchar (CTO & Co-Founder)

## Core Business
- **Primary industry/industries**: AI Engineering & Consulting, focused exclusively on Financial Services (banking, capital markets, wealth management, insurance)
- **Target customers**: B2B — Fortune 500 financial institutions, large banks, wealth management firms, insurance companies, and adjacent regulated industries
- **Mission or purpose statement**: To help Financial Services organizations move from AI-curious to AI-enabled by building capability and deploying systems across core workflows — translating AI ambition into secure, scalable solutions that deliver commercial value.

## Products & Services
- **Custom AI Agents**: Deploy custom AI agents from discovery through production, enabling automation across core workflows with full auditability, traceability, and governance controls built in from the start. Decision logic is aligned with business rules and policies, designed for high-value workflows where accuracy, compliance, and scale are critical.
- **AI Training & Enablement**: Build AI fluency across teams, from engineering to the C-suite, through hands-on training grounded in the client's core workflows. Separate tracks for business and technical teams, with governance, compliance, and risk embedded from the start.
- **Continuous AI Delivery**: Post-deployment support with Forward-Deployed Engineers working alongside client teams to ensure systems continue to evolve. Governance, monitoring, and performance tracking are built in from the start, with AI adoption compounding across workflows over time.

## Market Standing
- **Valuation/Market Cap**: Not publicly available (private, self-funded boutique firm)
- **Key Metric**: Total Funding — Not disclosed (bootstrapped); the team reports 100+ AI implementations since 2019, including with Fortune 500 firms, and a team of 50+ AI engineers, architects, and analysts across Europe
- **Notable Investors/Partners**: Anthropic (partner), AWS (Advanced Partner, among first 15 to achieve AWS GenAI Competency in Agentic AI), Google Cloud (Partner)
- **Growth Signals**: Achieved AWS AI Competency in the Agentic AI category; named clients include HSBC (aligned 50+ senior leaders on AI deployment and governance) and Visa (scaled marketing operations across 9+ markets with LLM-based content system); works with leading Asian banks on agentic AI assistants for relationship managers

## Competitive Advantages
- **FSI Domain Expertise**: Deep specialization in Financial Services (banking, capital markets, wealth management, insurance) means no translation layer between business intent and technical execution. Compliance, governance, and risk are built into system design from the start.
- **Embedded Co-Creation**: Unlike traditional consultancies, they co-create with client teams through embedded delivery, working alongside internal stakeholders to transfer knowledge and expand impact from inside the organization.
- **Accelerated Delivery**: Pre-built FSI agent components and proven deployment patterns allow delivery of working, production-grade systems in weeks rather than months.
- **Boutique Senior Teams**: Small, senior teams (50+ engineers/architects) rather than large junior-heavy teams, enabling high-quality, fast execution.

## Strategic Focus
- Expanding agentic AI adoption within regulated financial environments, focusing on production-grade systems that operate within existing governance frameworks
- Building on partnerships with Anthropic, AWS, and Google Cloud to deliver cutting-edge AI solutions
- Deepening presence in wealth management, retail/corporate banking, private banking, SME banking, and insurance segments
- Scaling through a two-tier talent model (Core Team + Talent Network) to remain agile while growing

## Why Work Here
- **Remote-first culture**: Work from anywhere, with flexible hours — "design your day" philosophy
- **Benefits**: Unlimited PTO, flexible schedule, work on projects incorporating the latest AI technology (Anthropic, AWS, GCP)
- **Culture**: 7 guiding principles including ownership, genuine connections, and leadership development. Small, senior teams with high autonomy and low bureaucracy
- **Growth**: Opportunity to work on cutting-edge agentic AI for Fortune 500 financial clients, with clear paths to leadership roles
- **Team structure**: Two collaboration options — Core Team (full-time, benefits-eligible) or Talent Network (project-based, minimum 0.33 FTE)
- **Interview process**: Fast, streamlined — application → screening interview → validation interview → qualification interview → reference check

## Sources
1. [neurons-lab.com](https://neurons-lab.com/about/)
2. [neurons-lab.com](https://neurons-lab.com/)
3. [neurons-lab.com](https://neurons-lab.com/careers/)
4. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/neurons-lab.com)
5. [neurons-lab.com](https://neurons-lab.com/careers/core-team/)

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