Adversarial AI, Offensive Security Analyst

Responsibilities

• Plan & execute AI-enhanced attacks: lead red team operations and penetration tests incorporating LLM-driven techniques and agentic automation to simulate sophisticated adversaries at scale.
• Maintain human-in-the-loop oversight to ensure safe, controlled execution.
• Custom tooling & automation: design or integrate custom offensive tools and scripts that use AI/ML (e.g., LLM APIs, automation frameworks) to accelerate vulnerability discovery, exploit development, and testing workflows.
• Develop internal frameworks that interface LLMs with red-team C2 platforms (such as MythicMCP, SliverMCP) to enable autonomous or semi-autonomous operations.
• Conduct adversarial simulation & fraud testing: exercise targeting both technical systems and fraud controls, using AI to emulate how attackers might abuse scale and automation.
• Work with fraud-risk teams to simulate large-scale abuse scenarios (bots, deepfakes, automated scams) using AI capabilities.
• Collaborate & purple team with blue teams (SOC, detection engineering) and fraud-prevention teams to share insights from AI-augmented tests.
• Help validate and improve detection and response for AI-driven attack techniques through joint purple-team exercises.
• Report findings: document attack scenarios, findings, and mitigations in clear reports; present results and risk insights to technical staff and executives, translating complex AI-augmented attack methods into actionable defense improvements.
• Mentor colleagues in adopting AI-assisted tools and foster a culture of innovation within the team.

• 5–7+ years of hands‑on experience in penetration testing, red teaming, or adversary simulation with a strong track record.
• Deep understanding of network/web application security, exploitation techniques, and attacker TTPs (MITRE ATT&CK).
• Proven experience leveraging AI/ML or automation in cybersecurity (e.g., using LLM APIs, scripting against AI services, building security chatbots or automation pipelines).
• Ability to craft effective prompts and interpret LLM outputs.
• Familiarity with integrating AI into tools or workflows (experience with frameworks like Model Context Protocol (MCP) servers is a strong plus).
• Proficiency in Python or similar languages for developing custom tools, automation scripts, and integrating APIs.
• Bachelor’s degree in computer science, engineering, or equivalent experience.
• Industry certifications such as OSCP, CRTE or similar are preferred, demonstrating advanced offensive skills.