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Senior Red Team Operator, AI/LLM

Join Armadin Security as a Senior Red Team Operator to redefine proactive security in the AI era with cutting-edge autonomous defense strategies.

Location
United States
Compensation
Not disclosed
Level
senior
Type
full time · Remote

Posted by employer 2 days ago

First seen on Joblaze 2 hours ago

Last verified on the company career page 2 hours ago

What you'll build

  • Run full kill chains
  • Prove what's exploitable
  • Map the blast radius
  • Hijack the agent
  • Poison the pipeline

Must have

  • Deep, hands-on AI offense
  • Application security fundamentals
  • Fluency with tools like Burp Suite
  • Proficiency across Linux, Windows, and macOS
  • Strong grasp of how LLMs work

Nice to have

  • You've built AI systems
  • You've shipped offensive AI tooling
  • You hunt in public
  • You keep sharpening
  • You know the frameworks

Practical constraints

  • Must be eligible to work in the United States without sponsorship
  • Ability to travel up to 20% for on-site engagements or client meetings

AI in the day-to-day

You already use AI tools to speed up recon, analysis, or tooling, and you're excited to push them much further.

Requirements

Visa
No sponsorship (stated in posting)

Not disclosed in this posting: compensation, years of experience.

Benefits

Flexible PTO Equity/Stock Options Health Insurance

Joblaze summary

In the role of Senior Red Team Operator at Armadin Security, the individual will engage in hands-on offensive operations, utilizing advanced techniques to exploit vulnerabilities across various AI systems and applications. Key skills include deep knowledge of AI attack vectors, application security, and proficiency in tools for testing and automation. This position is ideal for seasoned professionals with a strong background in red teaming and a proactive mindset, eager to contribute to a small, agile team focused on redefining security in the AI landscape.

Joblaze insights

  • Listed today — first seen on Joblaze October 19, 2026. Last confirmed on Armadin Security's careers page October 19, 2026.
  • Python appears in 41.7% of 338 comparable senior security roles in United States; LLM appears in 0.3% of 338 comparable senior security roles in United States.

Quick facts

Is the Senior Red Team Operator, AI/LLM role remote?
Yes — Armadin Security lists this as a fully remote position.
What's the tech stack?
Joblaze extracted these technologies from the posting: AI/ML, Adversarial Robustness Toolbox, Burp Suite, Caido, Garak, LLM.
What seniority level is this role?
Armadin Security targets senior candidates for this position.
Is this full-time or contract?
Full-time for this Senior Red Team Operator, AI/LLM role at Armadin Security.

From the original posting

Role Overview

Every operator knows the ending. You found the path, chained it to access the crown jewel, and wrote it up. Then the engagement closed, the environment changed the next week, and nobody walked that kill chain again until next year's test. Armadin is built to end that cycle.

We're building an agentic attacker swarm. These are specialized AI agents running custom models that discover, exploit, and chain vulnerabilities across external, internal, identity, cloud, and application attack surfaces. They run continuously, at machine speed, against live production environments. Until recently, nobody could put a nation-state-caliber adversary inside every network around the clock. That's the hyperattack, and it's what we do. In one recent engagement, the swarm generated more than 17 million offensive actions in three days and validated 38 attack paths. That's the scale your tradecraft will run at.

As a Senior Red Team Operator, your tradecraft is the training signal. You'll run hands-on operations, and then you'll encode how you think into the reasoning our agents use at scale: how you pick a foothold, abuse an application's logic, pivot through identity, separate what's actually exploitable from scanner noise, and map the blast radius once you're in. You'll pressure-test full kill chains end to end, find where the swarm is naive, and teach it to move like an expert, using AI throughout your own workflow to go faster and deeper.

The problems here are new ones:

  • What does a kill chain look like when it can branch a thousand ways in parallel?

  • How do you prove exploitability instead of just flagging it?

  • What does a continuous hyperattack find that a two-week engagement never could?

You'll work alongside operators who have spent decades on the front lines of red teaming and the AI engineers turning that experience into autonomous defense. It's a small, scrappy group with high ownership, and the work you ship reaches enterprises through industry leading partners. If you've ever finished an op thinking "imagine running this everywhere, all the time," this is where you get to do it.

What You'll Do

  • Run full kill chains, not checklists. Attack LLMs, agents, RAG pipelines, and the applications wrapped around them the way a real adversary would. Chain prompt injection, tool abuse, and integration flaws into paths no guardrail benchmark ever sees.

  • Prove what's exploitable. Turn a jailbreak or odd model behavior into a working, reproducible exploit with clear evidence of impact. Separate real risk from refusal-bypass noise.

  • Map the blast radius. Follow every foothold to its end, from a poisoned document or hijacked agent to tool calls, credentials, identity, cloud, and data. Show what an attacker could actually reach through the model.

  • Hijack the agent. Abuse tool and function calling, MCP servers, plugins, memory, and multi-agent handoffs to make agents act against their owners. Target excessive agency, confused-deputy flaws, and broken trust boundaries.

  • Poison the pipeline. Attack the full AI supply chain, including training and fine-tuning data, embeddings and vector stores, model weights and serialization formats, and the MLOps infrastructure that ships them.

  • Read code like an attacker. Review agent frameworks, orchestration logic, and model-serving code for injection paths, unsafe output handling, and insecure design, using AI-assisted analysis to cover more ground faster.

  • Take models apart. Use black-box probing, adversarial input generation, model extraction, and membership inference to understand how a model really behaves and what it leaks.

  • Train the swarm. Translate your AI attack tradecraft into the techniques, decision logic, and evaluation data that make our agents operate like expert operators at machine speed.

  • Break the machine. Pressure-test the swarm's own models and agents, spot where they're naive, manipulable, or wrong, and make the human-in-the-loop calls that keep autonomous operations safe and accurate.

  • Build what's missing. Write scripts, harnesses, and tools that automate AI attacks at scale, and treat AI as a daily force multiplier in your own workflow.

  • Tell the attack story. Deliver reports and debriefs that walk engineers, executives, and counsel through the attack narrative, the business risk, and the fix.

  • Stay ahead of the threat. Research emerging AI attack classes, from indirect prompt injection to agent-to-agent exploitation, and feed what you learn straight into the platform.

  • Own your work. Balance concurrent operations and internal research with startup-level autonomy, shifting priorities as fast as the threat does.

What You'll Bring

  • Deep, hands-on AI offense. LLM and agent exploitation beyond the OWASP Top 10 for LLM Applications, including direct and indirect prompt injection, jailbreak chains, system prompt extraction, and data exfiltration through model outputs.

  • Depth in at least one more AI attack surface:

    • Agentic systems: tool and function-call abuse, MCP and plugin exploitation, memory poisoning, and multi-agent trust boundaries.

    • RAG and data pipelines: vector store and embedding attacks, retrieval poisoning, and cross-tenant data leakage.

    • Adversarial ML: evasion, model extraction, membership inference, and training or fine-tuning data poisoning.

    • AI supply chain and infrastructure: malicious model artifacts, unsafe serialization, model registries, and inference and MLOps platforms.

  • Application security fundamentals. Most AI attacks land through the app around the model. You bring strong web and API exploitation skills, including authentication and authorization bypass and injection chains.

  • An attacker's toolkit and the instinct to go beyond it. Fluency with tools like Burp Suite, Caido, Garak, PyRIT, promptfoo, and the Adversarial Robustness Toolbox, plus the judgment to know when the tool is wrong.

  • Systems understanding that makes chaining possible. Strong grasp of how LLMs, agent frameworks, and model-serving stacks work under the hood, and how one weakness leads to the next.

  • Comfort everywhere you land. Proficiency across Linux, Windows, and macOS and major cloud AI platforms, with scripting in Python, Bash, or similar.

  • An AI-forward way of working. You already use AI tools to speed up recon, analysis, or tooling, and you're excited to push them much further.

  • Sharp human-in-the-loop judgment. You absorb new information quickly, make time-sensitive calls under pressure, and can explain your reasoning to safety and engineering teams.

  • A builder's, startup mindset. You're scrappy, self-directed, and comfortable with ambiguity, and you'd rather ship something useful than wait for a process.

  • Clear communication with clients. You can translate a complex AI attack chain for engineers, executives, and legal counsel during engagements and remediation debriefs.

  • Logistics. Must be eligible to work in the United States without sponsorship. Ability to travel up to 20% for on-site engagements or client meetings.

Even Better

  • You've built AI systems, not just broken them. Hands-on work with LLM fine-tuning, agentic frameworks like LangChain or AutoGen, or ML pipelines gives you an insider's view of where they fail.

  • You've shipped offensive AI tooling. Prompt injection harnesses, jailbreak fuzzers, Burp or Caido extensions for LLM traffic, or open-source AI red team projects others actually use.

  • You hunt in public. AI bug bounty findings, CVEs in AI frameworks or model-serving platforms, AI CTF wins, or conference talks at venues like DEF CON's AI Village.

  • You keep sharpening. Active time on AI security challenges like Gandalf, Crucible, HackAPrompt, or PortSwigger's LLM attack labs.

  • You know the frameworks. Working familiarity with MITRE ATLAS, the OWASP Top 10 for LLM Applications, and the NIST AI Risk Management Framework.

  • Relevant certifications, such as OSWE, OSAI, Burp Suite Certified Practitioner, GWAPT, or emerging AI security credentials.

Location

Remote in United States

Benefits & Perks | FTE

🏥 Full Health, Dental, & Vision Coverage

📈 Meaningful Equity Ownership

🥙 In-Office Meals

💸 401(k), HSA, and FSA Plans

🌴 Flexible PTO

 

Standard company text repeated across Armadin Security's postings is omitted here.

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