Hire senior AI Security & Red Teaming Specialists from Latin America

Hire AI Security & Red Teaming Specialists

Specialists who attack your AI systems before someone else does — prompt injection, jailbreak resistance, data exfiltration paths, tool misuse in agents — and turn the findings into guardrails and regression evals. Onboarded in one week.

About the role

The AI Security & Red Teaming Specialist role consists of attacking your AI systems the way a real adversary would — prompt injection, jailbreaks, data exfiltration through completions, agents tricked into misusing their tools — then turning every finding into guardrails and regression evals. Classic pentesting checks your infrastructure; this role checks your model's behavior. If your AI touches customers, money, or sensitive data, someone will eventually run these attacks. Better if you paid them to.

Monthly rate

$7,000–$10,000/mo

All-in: contract, benefits, equipment, IP

Experience

10+ years security

2+ in AI red teaming

Location

Latin America

Argentina · Colombia · Mexico

Timezone

Full US overlap

Fluent English, onboarded in one week

Core stack

Prompt injection testingAgent tool-misuse auditsAdversarial evalsGuardrail designClaude API / OpenAIData-flow and access review

AI tools, daily

Claude CodeAnthropic ConsoleEvals frameworks

Verticals seen

FintechHealthcareLegal & complianceEnterprise agentic

What they own — and what they don't

What they own

  • Run adversarial audits on LLM features and agents: injection, jailbreaks, exfiltration paths, tool misuse
  • Test agentic systems for privilege escalation — can an agent be talked into touching what it shouldn't?
  • Review the AI attack surface: data flows, credential models, tenant isolation, what leaves the perimeter
  • Turn findings into working defenses: guardrails, filters, and adversarial eval suites wired into CI
  • Re-test after every significant model, prompt, or tool change — the surface reopens each release

What they don't — and who does instead

  • Design the agent architecture — that's the AI Agent Architect; this role tries to break it
  • Build the product features being tested — that's AI Engineering
  • Replace your infrastructure pentest or SOC 2 audit — this is a complementary surface, not a substitute
  • Rubber-stamp launches — if it's not safe to ship, the report says so

Who hires this role, and for what

  • Fintech and healthcare shipping customer-facing AI. Regulated data plus generative output is the highest-stakes combination there is. They audit before launch because the alternative is explaining a leak to a regulator.

  • Companies deploying agents with real permissions. Once an agent can write to systems or move money, 'what could someone make it do?' becomes a board-level question that needs a tested answer.

  • AI product companies selling to enterprises. Enterprise security teams now ask for AI-specific testing evidence in procurement. A red-team report is becoming table stakes to close.

  1. 01

    Pre-launch adversarial audit. Full attack pass on a customer-facing assistant or agent before it meets real users — findings, reproducible cases, fixes.

  2. 02

    Agentic platform security review. Credential isolation, tool permission boundaries, cross-tenant leakage — the specific ways multi-agent systems fail.

  3. 03

    Standing adversarial evals. Attack cases wired into CI so every prompt or model change gets red-teamed automatically, not just at launch.

  4. 04

    Procurement and compliance evidence. The AI-specific security documentation enterprise buyers and auditors now expect to see.

Work our engineers at this role have shipped

  • Pre-launch adversarial audit of a customer-facing LLM assistant — injection, exfiltration, and off-policy behavior mapped and closed
  • Security review of a multi-tenant agentic platform: credential isolation, tool permission boundaries, cross-tenant leakage tests
  • Standing adversarial eval suite added to a client's CI so every model or prompt change gets red-teamed automatically

Do you actually need an AI Security & Red Teaming Specialist?

You do, if:

  • Your AI feature is customer-facing and handles data you'd have to disclose losing
  • Agents in your stack hold credentials or tool access an attacker would want
  • An enterprise prospect's security questionnaire has an AI section you can't answer yet
  • Nobody has ever seriously tried to prompt-inject your own system

You probably don't, if:

  • Your AI is internal-only, read-only, on non-sensitive data — basic guardrails from your AI Engineer may be enough for now
  • You haven't built anything yet — bring this role in before launch, not before the prototype
  • What you need is general infra security — hire a classic security engineer first

Not sure which role fits? Tell us the problem instead of the title — we'll tell you what we'd actually staff, even if it's not this. If it is this: discovery call today, matched profiles in 48 hours, onboarded in a week.

Hire a Senior AI Security & Red Teaming Specialist

Common questions

  • Classic pentesting attacks your infrastructure. AI red teaming attacks your model's behavior: prompt injection through user content, agents tricked into misusing their tools, sensitive data leaking through completions. Different attack surface, different methodology, different findings. Most pentest firms don't cover it yet.

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