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# AI Red Team Engineer

- **Company:** White Circle
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Software System Penetration Testing, Automation of Tests, Burp Suite, Software as a Service, Information Leak Prevention, Open Web Application Security, Regression Testing, Software Safety, Web Applications, Datadog, Scripting, Cloud Platform System, Postman, LangChain, Retrieval-Augmented Generation, Large Language Models, Software Security, Llamaindex, Agentic-AI, Pytest, Bug Reporting, HuggingFace, Playwright, Sentry, DeepMind, Web Api
- **Published:** October 27, 2026
- **Apply:** https://www.builtincolorado.com/auth/login?destination=/job/ai-red-team-engineer/11887283

## About the Role

* Experience with Burp Suite, Postman, Playwright, pytest.
 * Experience with modern LLM red-teaming automated agents and pipelines.
 * Familiarity with LangChain, LangGraph, LlamaIndex, RAG pipelines, AI agents, tool/function calling, and LLM-as-judge evaluation.
 * Familiarity with OWASP LLM Top 10, OWASP Web Top 10, MITRE ATLAS, or other AI security taxonomies.
 * Experience testing RAG systems, AI agents, tool-calling workflows, browser agents, or internal copilots.
 * Experience writing customer-facing security reports.
 * Experience with trust & safety, abuse prevention, fraud, moderation, or platform security.
 * Experience building eval pipelines, regression suites, dashboards, or CI-friendly security tests.
 * A track record in CTFs, red-team competitions, or responsible-disclosure / bounty programs.

## Description

Hiring Remotely in United States
Entry level
In-Office or Remote
Hiring Remotely in United States
Entry level
Conduct end-to-end adversarial testing of LLM-powered systems, including jailbreaks, prompt injection, data leakage, tool misuse, and policy bypasses. Automate attacks with Python, build reusable attack libraries and regression tests, document reproducible security findings, and translate model failures into product requirements and customer-facing evidence. Support demos, security reviews, and sales conversations while tracking emerging AI security techniques.
The summary above was generated by AI

TL;DR: We're looking for an AI Red Team Engineer to break LLM-powered systems responsibly, automate repetitive attacks, and turn their findings into clear evidence that powers customer demos, security reviews, and sales conversations. You'll own hands-on adversarial testing end to end: find the failure, prove it, script it, and write it up., * Red-team LLM-powered systems: chatbots, copilots, RAG pipelines, AI agents, tool-calling workflows, and API-based AI products.

 * Test for jailbreaks, prompt injection, system-prompt and tool leakage, sensitive-data and context leakage, unsafe outputs, policy bypass, tool misuse, excessive agency, resource and token-cost abuse, and business-logic abuse.
 * Write lightweight Python to automate attacks, run prompt sets, call model APIs, collect and score responses, and generate repeatable reports.
 * Build and maintain an internal attack library: prompts, scenarios, test cases, regression tests, scoring rubrics, and reusable demo cases.
 * Turn model failures into clear reports: what happened, why it matters, how to reproduce it, how severe it is, and how to fix it.
 * Convert successful attacks into regression tests and product requirements.
 * Track new red-team and safety techniques and fold the useful ones into our tests.
 * Support GTM by producing strong, credible evidence for customer demos, security reviews, and sales conversations.

You'll fit right in if you

 * Genuinely love breaking things and reasoning adversarially.
 * Have a background in QA automation, AppSec, API/security/pen testing, or bug bounty.
 * Have strong Python scripting skills.
 * Have experience testing APIs, web apps, backends, or SaaS products.
 * Are hands-on with LLMs, prompts, system instructions, RAG, agents, and tool/function calling.
 * Understand LLM-specific abuse vectors (prompt injection, jailbreaks, data leakage, tool misuse, excessive agency, token-cost exhaustion).
 * Can find bypasses, abuse edge cases, chain failures, and reason about real-world impact.
 * Can separate real customer risk from low-impact prompt tricks.
 * Write clear, reproducible bug reports in clear English.
 * Can move fast without perfect requirements.
 * Hold a firm ethical line: you red-team to make systems safer, operate within scope and the law, and don't produce or traffic in genuinely harmful material.