Senior Red Team Operator, AI/LLM
New
A
ArmadinAI security
Remote in United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonBash
Requirements
- Hands-on experience exploiting LLMs and agents, including prompt injection, jailbreak chains, system prompt extraction, and data exfiltration through model outputs.
- Depth in at least one additional AI attack surface: agentic systems, RAG and data pipelines, adversarial ML, or AI supply-chain and infrastructure.
- Strong web and API exploitation skills, including authentication and authorization bypass and injection chains.
- Fluency with tools such as Burp Suite, Caido, Garak, PyRIT, promptfoo, and the Adversarial Robustness Toolbox.
- Strong understanding of LLMs, agent frameworks, and model-serving stacks.
- Proficiency across Linux, Windows, and macOS, and major cloud AI platforms.
- Scripting experience in Python, Bash, or similar.
- Experience using AI tools to accelerate reconnaissance, analysis, or tooling.
- Ability to make time-sensitive decisions and explain reasoning to safety and engineering teams.
- Ability to communicate complex AI attack chains to engineers, executives, and legal counsel.
- Must be eligible to work in the United States without sponsorship.
- Ability to travel up to 20% for on-site engagements or client meetings.
Responsibilities
- Run end-to-end attacks against LLMs, agents, RAG pipelines, and surrounding applications.
- Turn jailbreaks and unusual model behavior into reproducible exploits with evidence of impact.
- Trace attack paths through tools, credentials, identity, cloud, and data to establish blast radius.
- Exploit tool and function calling, MCP servers, plugins, memory, and multi-agent handoffs.
- Attack AI supply-chain components, including training data, embeddings, model artifacts, and MLOps infrastructure.
- Review agent frameworks, orchestration logic, and model-serving code for vulnerabilities.
- Use black-box probing, adversarial inputs, model extraction, and membership inference to assess model behavior.
- Translate AI attack tradecraft into techniques, decision logic, and evaluation data for agents.
- Pressure-test agent models and make human-in-the-loop safety and accuracy decisions.
- Build scripts, harnesses, and tools to automate AI attacks, and report attack narratives, business risks, and remediation.
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