AI Security Engineers for LLM and Agent Risk

Hire AI Security Engineers
Who Secure AI Before It Scales

Hire AI Security Engineers who test and harden LLM apps, RAG systems, agents, model gateways, prompts, tools, logs, and data flows before AI risk becomes a production incident.

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Senior AI Security Engineer

OWASP LLM Top 10 Red Teaming DLP Threat Modeling
All Levels

$7,500/mo

Junior from $3,500/mo · Mid from $5,200/mo · Senior from $7,500/mo

7-Day Risk-Free Trial

Zero commitment start

Onboard in 48 Hours

Pre-vetted, ready to ship

AI-Native Development

Faster iteration, cleaner code

Trusted by CTOs, Engineering Leaders & Operators Worldwide

Trusted by CTOs, Engineering Leaders & Operators Worldwide

Trusted by CTOs, Engineering Leaders & Operators Worldwide

Trusted by CTOs, Engineering Leaders & Operators Worldwide

Trusted by CTOs, Engineering Leaders & Operators Worldwide

10+ Years in Business

500+ Projects Delivered

200+ Global Clients

4.9/5 Client Satisfaction

Why Companies Struggle to Hire AI Security Engineers

AI security expands the attack surface beyond traditional AppSec. Prompts, retrieved content, tool calls, model outputs, vector stores, logs, plugins, agents, and human review paths all need controls.

The Hiring Problem

LLM apps are exposed to direct and indirect prompt injection, sensitive information disclosure, insecure output handling, system prompt leakage, and unbounded usage costs

Agents can call tools with too much authority, weak approval boundaries, unclear audit trails, or no blast-radius limit when a malicious input changes behavior

RAG systems can leak confidential records across tenants, roles, accounts, document scopes, metadata filters, or vector-store boundaries

Traditional AppSec checks often miss AI-specific risks such as excessive agency, data and model poisoning, vector and embedding weaknesses, and model-output trust failures

Our Solution

Engineers test against OWASP LLM Top 10 risks, MITRE ATLAS-style attack paths, and your actual product workflows instead of generic jailbreak lists

Agent permissions use least privilege, scoped tools, action allowlists, approval gates, secret isolation, rollback paths, and audit trails

RAG controls protect tenant, role, document, row-level, and source-system boundaries with permission-aware retrieval and leakage tests

Red-team findings become concrete fixes, monitoring rules, regression tests, incident playbooks, and release gates leadership can inspect

Why Hire AI Security Engineers from Devlyn

Senior, product-minded AI Security Engineers vetted for adversarial thinking, application security, model behavior, data protection, secure agent design, and practical mitigation ownership.

Why Hire AI Security Engineers from Devlyn
Prompt Injection Testing

Prompt Injection Testing

Tests direct, indirect, retrieval-based, tool-mediated, and multi-turn prompt injection against real workflows, not only canned jailbreak prompts.

Agent Permission Design

Agent Permission Design

Scopes tools, actions, secrets, permissions, approval rules, idempotency, rollback paths, and audit trails for agentic systems.

RAG Data Leakage Review

RAG Data Leakage Review

Checks retrieval boundaries, metadata filters, citations, logs, tenant isolation, row-level permissions, and source-system authorization.

AI Threat Modeling

AI Threat Modeling

Maps assets, model behavior, prompts, tools, data flows, trust boundaries, abuse paths, and control gaps across the AI lifecycle.

Red Teaming

Red Teaming

Runs adversarial tests for jailbreaks, prompt leaks, data exfiltration, unsafe outputs, excessive agency, poisoning, model abuse, and cost attacks.

Security Monitoring

Security Monitoring

Adds alerts for policy bypasses, suspicious prompts, tool abuse, leakage patterns, prompt leakage, unsafe output, anomalous retrieval, and cost attacks.

How hiring actually works.

No procurement cycle, no mystery shortlists. Six steps from first call to first shipped feature, with timelines you can defend to leadership.

A 30-minute call to map the business problem, current stack, success metrics, security constraints, timezone overlap, and why the AI Security Engineer role is the right hire. If another role or engagement model would reduce risk, we say that before you interview anyone.
AI Security Engineer Scoping Call
Within 24 hours, you receive pre-vetted AI Security Engineer profiles matched against prompt injection, data leakage, model abuse, tool permissions, red-team methods, logging, and mitigation design. Each profile includes technical context, availability, communication fit, and the reason we believe the engineer belongs in your interview loop.
AI Security Engineer Shortlist
Use the interview loop to test prompt injection, data leakage, model abuse, tool permissions, red-team methods, logging, and mitigation design. You can run system design, live review, portfolio walkthrough, or a paid task based on your real work.
Interview for AI Security Engineer Fit
NDA and IP assignment are completed first. Then we set up AI threat model, prompts, tools, access policies, logs, sensitive data paths, and the first security test plan so the engineer can contribute without a week of hand-holding.
Onboard Into the AI Security Engineer Workflow
By day 7, you see an AI security assessment or mitigation with exploit examples, control recommendations, logging gaps, and priority fixes. Progress is visible before the trial becomes a long commitment.
First AI Security Engineer Proof Point
During the risk-free trial, you evaluate security judgment, adversarial thinking, documentation quality, and ability to reduce AI risk without stopping useful product work. If the fit is wrong, we replace the engineer within 48 hours.
AI Security Engineer Trial Check

AI Security Engineer: Engagement Options

Three transparent ways to engage. All rates are in USD and exclude taxes. No recruitment fees, no notice periods.

Red Team

AI Security Assessment

$22,000

fixed

3 weeks, senior AI security engineer

  • →Threat model document
  • →Adversarial test report
  • →Remediation plan
  • →Compliance mapping (NIST / EU AI Act)
Talk to Sales

Embedded

Senior AI Security Engineer

$7,000

/mo

Full-time, 5–10+ years

  • →Owns AI security program
  • →Designs guardrails and authz
  • →Continuous red team
  • →Audit-ready documentation
Talk to Sales

Security Pod

AI Security + AppSec + DevSecOps

$22,000

/mo

3-person pod, 3–6 months

  • →AI + application + platform security
  • →Continuous testing + monitoring
  • →Compliance evidence
  • →On-call rotation
Talk to Sales

Where AI Security Engineers Create Leverage

From SMEs and scaling companies to enterprise teams. Same senior bar; different shape of engagement.

01.

LLM App Security Review

Assess customer-facing assistants, copilots, chat workflows, and AI features for prompt injection, output trust, leakage, abuse, monitoring, and release readiness.

02.

Agent Permission Audit

Review tool access, approval rules, secrets, action logs, rollback paths, idempotency, escalation rules, and blast radius for agentic workflows.

03.

RAG Leakage Test

Test whether users can retrieve data they should not see across tenant, account, document, role, metadata, source-system, and vector-store boundaries.

04.

AI Red Team Sprint

Run focused adversarial testing and turn findings into fixes, regression cases, monitoring rules, release gates, and incident playbooks.

What should change after you hire AI Security Engineers

A CTO is not hiring AI Security Engineers for a generic penetration-test PDF. The engagement should expose how your AI system can be abused, fix the controls that matter, and leave your team with repeatable security practices for future AI releases.

Outcome 01 AI Security Engineer capability that reaches production
+

The first meaningful outcome is a security finding and mitigation path tied to a real AI surface area. That might be a prompt-injection exploit against a customer-facing assistant, a RAG leakage test across account boundaries, an agent permission audit, or a red-team sprint against tool use and output handling. The proof is not a list of theoretical risks; it is exploit evidence, impact, fix priority, and a control plan your engineers can implement.

Evidence to expect: an AI security assessment or mitigation with exploit examples, impact notes, control recommendations, logging gaps, and priority fixes

Outcome 02 AI Security Engineer risks handled before scale
+

The real hiring risk is an AI product that exposes data, follows malicious instructions, trusts unsafe outputs, gives agents excessive authority, leaks prompts, misses tenant boundaries, or ships without threat modeling. We reduce that risk through permission-aware retrieval, tool allowlists, approval gates, output validation, DLP checks, secret isolation, prompt-injection regression tests, abuse monitoring, logging, and incident-response paths.

Evidence to expect: You should see explicit tradeoffs, known failure modes, exploit reproduction notes, unresolved control gaps, and a next-decision list instead of optimistic delivery language.

Outcome 03 AI Security Engineer metrics a CTO can inspect
+

The engagement should be judged by exploitable findings closed, prompt-injection bypass rate, sensitive-data exposure risk, RAG isolation coverage, tool-permission coverage, output validation coverage, logging visibility, monitoring coverage, cost-abuse controls, unresolved critical risks, and release gates passed.

Evidence to expect: We define the inspection points early so you can decide whether to continue, scale, pause, or replace based on evidence.

Outcome 04 AI Security Engineer knowledge your team keeps
+

A strong AI Security Engineer engagement should leave your team with reusable security assets: AI threat models, test prompts, exploit cases, mitigation notes, permission patterns, retrieval isolation checks, red-team reports, monitoring rules, incident runbooks, and release-review criteria.

Evidence to expect: Expect documentation tied to the work itself: architecture notes, decision records, handover material, and ownership boundaries your team can maintain.

How to decide if Devlyn is the right partner for AI Security Engineers

Choose us when

You need an AI Security Engineer when an LLM app, RAG workflow, model gateway, AI agent, or customer-facing copilot is close to production and you need real exploit testing plus practical controls.

Interview for

Use the interview to test prompt-injection methodology, data-leakage reasoning, RAG access-control design, model-abuse scenarios, tool-permission boundaries, red-team reporting, monitoring design, and how the engineer turns findings into fixes.

Expect clarity on

Scope, AI surface area, data sensitivity, tool authority, source-code access, logs, test environments, red-team boundaries, disclosure rules, IP assignment, security constraints, timezone overlap, and what proof should exist by day 7.

Do not accept

A generic shortlist, vague AI safety claims, unclear pricing, jailbreak-only testing, no exploit evidence, no mitigation ownership, weak code review, or a vendor who cannot explain how AI security findings become release gates.

Delivery governance and risk control

Devlyn is positioned as a senior AI and software engineering partner, not a resume marketplace. You get structured onboarding, secure access, NDA and IP assignment support, communication overlap, replacement flexibility, and delivery governance built around the outcome you are hiring for.

For this AI Security Engineer engagement, governance means threat models, attack examples, mitigations, access policies, tool boundaries, logs, monitoring rules, and release criteria are recorded. The engineer should not only find flaws. They should show how the flaw could affect users or data, what control reduces the risk, what remains unresolved, and how your team can retest the issue after future prompt, model, retrieval, or agent changes.

Ready to Hire an AI Security Engineer?

Share your AI surface area, data sensitivity, tools, and launch timeline. We will shortlist engineers who understand LLM, agent, and RAG security risk.

Hire AI Security Engineer Now Book a Free Consultation

hello@devlyn.ai

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Frequently Asked Questions

Answers for CTOs, engineering leaders, product leaders, operators, and hiring managers comparing senior engineering capacity, delivery models, risk controls, and long-term ownership.

You can usually start the hiring conversation immediately and receive a shortlist within 24 hours after we understand your product, stack, timeline, and seniority needs. The goal is not to send resumes quickly; it is to send AI Security Engineers who match the outcome, risk profile, and communication bar for the role.

Yes. You interview the shortlisted engineers before committing. We recommend using the interview to test prompt injection, data leakage, model abuse, tool permissions, red-team methods, logging, and mitigation design. That makes the selection practical for a CTO instead of resume-led.

The first week should produce visible proof that the engineer understands your AI attack surface and can move risk down. You should see an AI security assessment or mitigation plan with exploit examples, impact notes, control recommendations, logging gaps, and priority fixes. If progress is unclear, you should know that early, not after a long contract cycle.

A strong hire should produce AI security controls for prompt injection, sensitive-data exposure, RAG leakage, tool abuse, unsafe output handling, model misuse, logging, red teaming, and mitigation. The outcome should be measurable through exploitable findings closed, bypass rate, RAG isolation coverage, tool-permission coverage, output-validation coverage, monitoring visibility, and unresolved critical risks.

Quality is managed through senior screening, role-specific interview criteria, code or architecture review, documented decisions, and delivery checkpoints. For AI security work, we look for proof across prompt-injection testing, RAG leakage review, agent permission design, AI threat modeling, red-team methodology, exploit documentation, mitigation design, logging, monitoring, and retest discipline.

Yes. The engineer joins your tools, repositories, standups, issue trackers, review process, and communication channels. For AI Security Engineer work, we define the operating model explicitly: threat models, attack examples, mitigations, access policies, tool boundaries, logs, monitoring rules, and release criteria are recorded.

Yes. Devlyn works with distributed teams and plans overlap windows for interviews, standups, reviews, and escalation. For AI Security Engineer engagements, the communication rhythm is tied to the proof points that matter: red-team findings closed, control coverage, logging visibility, policy bypass rate, sensitive-data exposure risk, and audit readiness.

NDA and IP assignment are handled before onboarding. Access is scoped to the tools, repositories, datasets, systems, or environments required for the AI Security Engineer scope, and sensitive work is governed through your security rules, audit expectations, and approval process.

Use the risk-free trial to evaluate whether the engineer can map the AI attack surface, reproduce prompt-injection or leakage risks, reason about tool permissions, document exploit impact, design mitigations, and communicate tradeoffs clearly. If the fit is wrong, we replace the engineer within 48 hours instead of forcing you through a long notice period or another sourcing cycle.

You can start with one specialist, add adjacent roles, or move into a pod model depending on the scope. Common expansion paths include product engineering, platform, data, security, QA, DevOps, or architecture support around the core AI Security Engineer work.

Typical options include AI Security Assessment ($22,000 fixed scope) 3 weeks, senior AI security engineer, Senior AI Security Engineer ($7,000/mo) Full-time, 5–10+ years, AI Security + AppSec + DevSecOps ($22,000/mo) 3-person pod, 3–6 months. We confirm the right model after discovery so you can compare dedicated hiring, a focused sprint, or a small pod against the risk and timeline of your actual AI Security Engineer requirement.

We can support both models. If you already have strong product and security leadership, the engineer can plug into your process. If you need more structure, Devlyn can add delivery oversight, sprint planning, reporting, and senior technical review around threat modeling, red-team tests, mitigation design, monitoring, logging, and release gates.

Devlyn reduces the hidden work of sourcing, vetting, onboarding, replacing, and governing specialist engineering talent. For AI security, that matters because the real risk is a product exposing data, accepting malicious instructions, trusting unsafe outputs, misusing tools, or shipping without threat modeling. You get a shorter path to qualified candidates and a trial structure focused on exploit evidence and mitigation quality.

Devlyn is a better fit when AI security work affects production systems, customer workflows, sensitive data, compliance, cost, or long-term maintainability. You get vetting, replacement support, delivery governance, IP protection, and continuity around the parts freelancers often skip: exploit reproduction, mitigation ownership, retesting, monitoring, runbooks, and release criteria.

An AI Security Engineer is usually the right hire when your AI system touches sensitive data, customer workflows, internal tools, model gateways, retrieval systems, or autonomous actions. Common use cases include LLM application security reviews, prompt-injection testing, RAG leakage tests, agent permission audits, AI red-team sprints, model-abuse prevention, secure prompt and tool design, DLP controls, monitoring rules, and release gates for AI features. If discovery shows you mainly need traditional AppSec, compliance policy, or MLOps hardening, we will say that before you hire.