AI Security Engineer

InvoiceCloud, Inc.

AI Security Engineer supporting Cybersecurity, Engineering, and Data Science teams at InvoiceCloud. Ensuring AI systems security, compliance and alignment with business objectives.

Posted 6/5/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $160,000 - $180,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudCyber SecuritySDLC

About the role

Key responsibilities & impact

Leads AI Security Architecture & Secure Design initiatives by designing and implementing lifecycle security controls across data ingestion, training, evaluation, deployment, and monitoring environments to measurably reduce AI-specific risk while maintaining product velocity.
Conducts structured Threat Modeling & Risk Assessment exercises for generative AI, RAG, and agent-based systems, evaluating risks such as prompt injection, data poisoning, model extraction, model inversion, abuse/misuse, and data leakage, and mapping findings to OWASP Top 10 for LLM Applications, MITRE ATLAS, and NIST AI RMF to drive remediation through engineering teams.
Defines and operationalizes Monitoring, Detection & Incident Response capabilities for AI systems by implementing prompt and output telemetry, tool-call logging, anomaly detection, and AI-specific incident response playbooks integrated into SIEM/SOC workflows.
Delivers measurable outcomes aligned to 30-, 150-, and 210-day milestones, including secure reference architectures, hardened AI environments, integrated security controls, and executive-ready reporting on AI risk reduction and posture maturity.
Establishes and formalizes AI Governance, Privacy & Third-Party Risk requirements by defining security expectations for AI use cases, third-party models, vendor integrations, and sensitive data usage, embedding controls into SDLC, procurement, and engineering standards.
Drives Cross-Functional Collaboration & Enablement by partnering with Engineering, Data Science, DevSecOps, Product, Legal/Privacy, and SOC teams to align on risk appetite, escalation paths, and secure design guardrails while raising AI security maturity across the organization.
Inventories current and planned AI/ML initiatives, documents system architectures and sensitive-data touchpoints, and implements a structured AI security intake and risk-rating process that ensures accountability and transparency.
Develops and communicates forward-looking 6- and 12-month AI security maturation plans that align technical priorities with business goals and clearly articulate risk trends, metrics, and investment needs to Security leadership and the CISO.
Integrates Secure MLOps / MLSecOps controls into AI delivery pipelines, including secure model registries, artifact signing and provenance validation, dependency scanning, secrets management, CI/CD guardrails, and hardened training and inference environments across AWS and Azure.
Builds and scales AI Security Testing & Red Teaming workflows by creating repeatable adversarial evaluation plans for jailbreaks, model evasion, prompt injection, and data exfiltration scenarios, ensuring security controls remain effective over time.
Develops automated regression test harnesses to continuously validate AI security protections as models, prompts, and dependencies evolve, reducing manual effort and improving coverage.
Establishes a sustainable AI security operating rhythm that includes intake reviews, threat modeling checkpoints, remediation tracking, and structured monitoring ownership to bring consistency and order to AI risk management.
Advances AI Security Testing & Red Teaming capabilities through adversarial experimentation and multi-dimensional analysis, proactively identifying emerging AI threat patterns before production impact.
Leverages AI and automation to strengthen testing coverage, automate regression validation, enhance anomaly detection logic, and improve the scalability of AI security monitoring and response.
Continuously evaluates emerging AI security research, tooling advancements, and regulatory developments, translating insights into adaptive defensive controls that support InvoiceCloud’s AI-first strategy while enabling responsible innovation.

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Cybersecurity, Engineering, Data Science, or related field (or equivalent practical experience).
5+ years of experience in security engineering, application/product security, cloud security, or DevSecOps.
2+ years of experience building or securing AI/ML systems (including LLM-based applications) in production environments.
Strong understanding of AI/ML threats and defenses, including prompt injection, data poisoning, model extraction, model inversion, adversarial inputs, data leakage, and abuse/misuse scenarios.
Experience integrating security into CI/CD and MLOps pipelines.
Proficiency with cloud platforms (AWS and Azure), container security, IAM, network segmentation, key management, and secrets management.
Familiarity with industry guidance such as OWASP GenAI/Top 10 for LLM Applications, MITRE ATLAS, and/or NIST AI RMF preferred.
Relevant certifications such as CISSP, CSSLP, CCSP, Azure Security certifications, or GIAC certifications preferred.

Benefits

Comp & perks

Base salary is one component of total compensation. Employees may also be eligible for an annual bonus or commission. Some roles may also be eligible for overtime pay.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

AI Security ArchitectureSecure DesignThreat ModelingRisk AssessmentIncident ResponseMLOpsAI Security TestingRegression TestingAnomaly DetectionData Protection

Soft Skills

Cross-Functional CollaborationCommunicationLeadershipOrganizational SkillsRisk Management

Certifications

CISSPCSSLPCCSPAzure Security CertificationsGIAC Certifications