Agentic AI Engineer (LLM, Python)

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The Job

About Us

We are a cybersecurity company building AI-native security platforms that span both offensive and defensive operations:

• Offensive Platform — An autonomous security platform that uses agentic AI to continuously penetration-test APIs, web applications, and cloud infrastructure. It validates vulnerabilities, chains attack paths, and generates audit-ready reports — all with human-in-the-loop safety

controls.

• Defensive Platform — A role-based AI SOC platform where specialized agents (SOC Manager, Analyst, Incident Response, Threat Hunter) work as a coordinated crew to triage alerts, investigate incidents, hunt threats, and manage Detection-as-Code — at machine speed, under human oversight.

We are not building wrappers around ChatGPT. We are building autonomous, multi-step AI agent systems that reason, plan, use tools, and take real-world action in high-stakes security environments.

The Role

As an Agentic AI Engineer, you will design, build, and ship the autonomous AI agent systems at the core of our products. Depending on your background and interest, you'll work on one or both of the following:

Offensive Security Agents: You'll build the agent brain that plans multi-step attack strategies, generates auditable configuration files, orchestrates security tool execution (Nmap, Nuclei, Nessus, custom exploits), validates discovered vulnerabilities, and chains minor findings into critical attack paths — all within a human-in-the-loop approval framework.

Defensive Security Agents: You'll build the specialized SOC agents that autonomously triage and correlate alerts, enrich events with contextual data, summarize investigations, propose detection rules, execute threat hunting queries, and recommend incident response actions — while keeping analysts in control of final decisions.

What You'll Do

• Design and implement autonomous agent architectures that handle multi-step reasoning, tool use, memory, and planning in production security workflows
• Build reliable agent loops with proper error handling, retry logic, guardrails, and human-inthe-loop approval gates for high-risk actions
• Develop dynamic tool-calling pipelines where agents select, configure, and orchestrate external security tools based on contextual analysis
• Engineer prompt chains and agent reasoning strategies across multiple LLM providers (OpenAI, Google Vertex AI, Anthropic Claude) with model-agnostic abstractions
• Build and maintain the context layer that gives agents awareness of users, assets, past incidents, typical behavior, and environmental state
• Design evaluation frameworks to measure agent reliability, accuracy, and safety — especially for high-stakes actions like exploit execution or incident response recommendations