InvoiceCloud
Fintech
AISecurityEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“AI Security Engineer at InvoiceCloud. Skills: AI Security, Application Security, Cloud-native security, AI/ML risk. Lead AI Security Architecture & Secure Design initiatives. Design lifecycle security controls”
What You'll Achieve.
Measurably reduce AI-specific risk; Deliver secure reference architectures; Deliver hardened AI environments; Deliver integrated security controls; Deliver executive-ready reporting on AI risk reduction; Deliver AI security maturation plans; Reduce manual effort; Improve coverage; Bring consistency and order to AI risk management; Proactively identifying emerging AI threat patterns
Industry & Context.
Structured problem solving; Root cause analysis
What They're Looking For.
Must Have
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, understanding of AI/ML threats and defenses, Experience integrating security into CI/CD and MLOps pipelines, Proficiency with cloud platforms (AWS and Azure), Proficiency with container security, Proficiency with IAM, Proficiency with network segmentation, Proficiency with key management, Proficiency with secrets management
Nice to Have
Familiarity with OWASP GenAI/Top 10 for LLM Applications, Familiarity with MITRE ATLAS, Familiarity with NIST AI RMF, CISSP certification preferred, CSSLP certification preferred, CCSP certification preferred, Azure Security certifications preferred, GIAC certifications preferred
What You'll Do.
Lead AI Security Architecture & Secure Design initiatives
Design lifecycle security controls
Implement lifecycle security controls
Conduct Threat Modeling & Risk Assessment exercises
Evaluate risks for generative AI
Map findings to OWASP Top 10 for LLM
Map findings to MITRE ATLAS
Map findings to NIST AI RMF
Drive remediation through engineering teams
Detection & Incident Response capabilities for
Implement prompt and output telemetry
Implement tool-call logging
Implement anomaly detection
Implement AI-specific incident response playbooks
Integrate AI capabilities into SIEM/SOC workflows
Deliver secure reference architectures
Deliver hardened AI environments
Deliver integrated security controls
Deliver executive-ready reporting on AI risk reduction
Establish and formalize AI Governance
Privacy & Third-Party
Define security expectations for AI use cases
Define security expectations for third-party models
Define security expectations for vendor integrations
Define security expectations for sensitive data usage
Embed controls into SDLC
Embed controls into procurement
Embed controls into engineering standards
Partner with Engineering
Align on risk appetite
Align on escalation paths
Align on secure design guardrails
Raise AI security maturity across the organization
Inventory current and planned AI/ML initiatives
Document system architectures
Document sensitive-data touchpoints
Implement a structured AI security intake process
Implement a structured AI risk-rating process
Develop forward-looking AI security maturation plans
Communicate AI security maturation plans
Integrate Secure MLOps / MLSecOps controls into AI
Implement secure model registries
Implement artifact signing and provenance validation
Implement dependency scanning
Implement secrets management
Implement CI/CD guardrails
Implement hardened training environments
Implement hardened inference environments
Build AI Security Testing & Red Teaming workflows
Scale AI Security Testing & Red Teaming workflows
Create repeatable adversarial evaluation plans
Ensure security controls remain effective
Develop automated regression test harnesses
Continuously validate AI security protections
Establish a sustainable AI security operating rhythm
Advance AI Security Testing & Red Teaming capabilities
Leverage AI and automation to strengthen testing coverage
Leverage AI and automation to automate regression validation
Leverage AI and automation to enhance anomaly detection
Leverage AI and automation to improve scalability of
Leverage AI and automation to improve scalability of
Continuously evaluate emerging AI security research
Continuously evaluate tooling advancements
Continuously evaluate regulatory developments
Translate insights into adaptive defensive controls
How You'll Work.
Team & Collaboration
Partner with Engineering; Partner with Data Science; Partner with DevSecOps; Partner with Product; Partner with Legal/Privacy; Partner with SOC teams
Communication Scope
Executive-ready reporting
Process & Methodology
SDLC, Procurement
Full Job Description
About InvoiceCloud: InvoiceCloud is a fast-growing fintech leader recognized with 20 major awards in 2025, including USA TODAY and Boston Globe Top Workplaces, multiple SaaS Awards wins for Best Solution for Finance and FinTech, and national customer service honors from Stevie and the Business Intelligence Group. Judges also highlighted our mission to reduce digital exclusion and restore simplicity and dignity to how people pay for essential services, as well as our leadership in AI maturity and responsible innovation. It’s an award-winning, purpose-driven environment where top talent thrives. To learn more, visit InvoiceCloud.com. Job Details: We are seeking a highly skilled and results-oriented AI Security Engineer to support the Cybersecurity, Engineering, and Data Science organizations. This role plays a critical part in advancing InvoiceCloud’s AI-first strategy by ensuring that AI/ML and generative AI systems are secure, resilient, compliant, and aligned with business objectives. This is role operates as a subject matter expert in AI security. The ideal candidate brings deep expertise in application security, AI/ML risk, and cloud-native security engineering, and serves as a trusted partner to Engineering, Product, DevSecOps, Legal/Privacy, and Security Operations. Success requires strong ownership, structured problem solving, cross-functional collaboration, and the ability to balance risk reduction with business velocity. Success Profile: This role is anchored in our company’s core competencies—These competencies reflect the mindsets and behaviors that define success in this role. We outline how each competency translates into real-world actions and outcomes specific to this role. Results Driven 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 ve
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