AI Security Engineer

TL;DR

AI Security Engineer (AI/LLM Security): Strengthen the security of AI-enabled products, internal AI tools, and agentic AI infrastructure across the AI lifecycle with an accent on identifying, assessing, and reducing AI-specific risks. Focus on building scalable AI security controls, detection logic, and governance-aligned safeguards while improving defenses against prompt injection, jailbreaking, RAG data poisoning, and data leakage.

Location: Hybrid (Denver, CO) — in office at least 3 days/week

Salary: $150,000–$178,000 USD

Company

hirify.global builds legal technology solutions and advances AI across its products and internal platforms.

What you will do

• Identify, assess, and reduce risks across the AI lifecycle for AI-enabled products, internal tools, and agentic AI infrastructure.
• Partner with Product, Engineering, GRC, IT, and Security to guide AI adoption, vendor/model integrations, data handling, and acceptable use.
• Defend against AI-specific threats such as prompt injection, jailbreaking, insecure outputs, RAG data poisoning, model abuse, data leakage, and indirect context manipulation.
• Improve security for LLM-connected application surfaces, including MCP server integrations, agent workflows, tool-call authorization boundaries, and SaaS interconnects.
• Build and maintain AI security controls, tooling, testing pipelines, detection logic, playbooks, and documentation to continuously validate AI behavior.
• Extend identity governance and secrets management to AI agents and non-human identities, including access scoping, credential lifecycle, inventory, deprovisioning, and audit logging.

Requirements

• 3–6 years of cybersecurity experience, including 1–2 years focused on AI/ML security, application security, or a related combination in a cloud-native SaaS or enterprise software environment.
• Working knowledge of LLM security risks and mitigations, including OWASP LLM Top 10 concepts (prompt injection, insecure output handling, RAG poisoning, DoS, and sensitive information disclosure).
• Hands-on application security fundamentals: threat modeling, SAST/DAST, API security, secrets management, secure SDLC, and CI/CD pipeline security.
• Technical proficiency with scripting/automation and AI/ML security tooling and platforms (e.g., LangChain, RAG pipelines, vector databases, Azure OpenAI, Amazon Bedrock).
• Experience securing Azure and/or AWS environments, including IAM, network segmentation, containers, Kubernetes, infrastructure-as-code scanning, and cloud-hosted AI workloads.
• Strong communication and analytical skills to explain complex AI security risks to technical and non-technical audiences and drive remediation independently.

Nice to have

• Certifications such as CAISP, CISSP, CEH, GPEN, or AWS/Azure security specialties.
• Experience with AI red teaming/adversarial ML testing tools (e.g., Garak, PyRIT, Counterfit) and/or AI bug bounty programs.
• Familiarity with AI governance frameworks and standards (e.g., ISO 42001, NIST AI RMF, EU AI Act, SOC 2 Type II, ISO 27001).
• Experience in B2B SaaS and regulated legal technology environments, including security reviews, RFP responses, due diligence questionnaires, or security-focused sales support.

Culture & Benefits

• Hybrid work in Denver with expectations to be in office at least 3 days per week.
• Collaborative team culture focused on integrity and support.
• Company bonus plan eligibility and comprehensive benefits package (medical, dental, vision, 401(k) with company match).
• Opportunity for growth through stretch assignments, visibility, and career development.
• Work at the intersection of legal technology and cutting-edge AI.

Hiring process

• Interviews to evaluate AI security expertise, application security fundamentals, and ability to communicate risks and drive remediation.
• Discussions to align on AI governance/security expectations and practical experience with cloud and AI security controls.