Join Our Team

Role Overview

We are a cybersecurity company building AI-native security platforms that span both offensive and defensive operations. Our offensive platform uses agentic AI to continuously penetration-test APIs, web applications, and cloud infrastructure, while our defensive platform is a role-based AI SOC where specialized agents triage alerts, investigate incidents, and manage detection-as-code at machine speed.

Key Responsibilities

• 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-in-the-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.
• Collaborate with security engineers to translate offensive and defensive domain expertise into agent behavior, tool profiles, and decision logic.
• Optimize for latency, cost, and token efficiency in production agent workloads.
• Support on-premise deployments using self-hosted open-source models (DeepSeek, Llama) for air-gapped enterprise customers.

Ecosystem Requirements

• 4+ years of software engineering experience with strong proficiency in Python.
• 1+ year of hands-on experience building LLM-powered agent systems (not chatbots, but agents that reason, plan, use tools, and take multi-step actions).
• Deep working knowledge of at least one agentic framework: LangGraph, LangChain, CrewAI, AutoGen, or equivalent.
• Experience with tool-calling / function-calling patterns, including dynamic tool selection and chaining.
• Solid understanding of prompt engineering for complex reasoning tasks (chain-of-thought, ReAct, plan-and-execute patterns).
• Experience integrating with multiple LLM providers (OpenAI, Anthropic, Google) and managing model-agnostic abstractions.
• Ability to build robust, production-grade systems, handling failure modes, retries, timeouts, guardrails, and observability.
• Strong fundamentals in distributed systems, async programming, and API design.
• Plus: Background in cybersecurity — offensive (pentesting, vulnerability assessment, red teaming) or defensive (SOC operations, SIEM, detection engineering, incident response).
• Plus: Familiarity with MITRE ATT&CK framework, Sigma rules, or detection-as-code practices.
• Plus: Experience deploying and fine-tuning open-source LLMs (Llama, DeepSeek, Mistral) for on-premise or air-gapped environments.
• Plus: Knowledge of graph databases (Neo4j) for modeling attack paths, or RAG pipelines and vector databases.

Compensation & Perks

• Opportunity to build AI agent systems for two security products simultaneously (offensive and defensive) — a rare engineering challenge.
• Direct influence on product architecture and AI strategy from day one.
• Work with a high-caliber team that understands both security and AI deeply.
• Competitive compensation packages and premium workspace setups.