Senior AI Security Engineer

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.

AI Security Engineer InvoiceCloud is a fast-growing fintech company with an award-winning culture and a leading disruptor in the electronic bill presentment and payment (EBPP) space. Serving more than 3,200 customers across the utility, government, and insurance industries, InvoiceCloud’s secure and innovative SaaS platform enhances the customer experience, driving higher digital payment, AutoPay, and paperless adoption rates. By switching to InvoiceCloud, clients can improve customer engagement and satisfaction while lowering costs, accelerating payments, and reducing staff workloads. To learn more, visit InvoiceCloud.com.

Excellence in technology, information security, and regulatory compliance are foundational to our success. InvoiceCloud has chosen an AI First approach with the technology augmenting human activities across the globe. The AI Security Engineer designs and implements security controls for AI/ML systems and generative AI capabilities, enabling safe innovation across InvoiceCloud products and internal operations. This role partners with Engineering, Data Science, Product, DevSecOps, and Security Operations to threat model AI use cases, build secure AI/ML delivery pipelines (MLSecOps), perform adversarial testing and AI red teaming, and ensure AI solutions meet security, privacy, and compliance expectations.

Mission The AI Security Engineer plays a key role in the InvoiceCloud Cybersecurity Program. This role requires strong attention to detail, persistence, expertise in application security and AI/ML risk, planning skills, self-motivation, organization, communication, and problem-solving abilities. The primary objective of this position is to consistently identify, prioritize, and reduce AI-specific security risks across the model lifecycle—data, training, evaluation, deployment, and operations—while maintaining business velocity and product quality.

Responsibilities

• AI Security Architecture & Secure Design

• Design and implement security controls for AI/ML and generative AI systems across the full lifecycle (data → training → evaluation → deployment → monitoring).

• Establish secure reference architectures for common patterns (e.g., retrieval-augmented generation (RAG), model gateways, tool/agent execution) with least privilege, data minimization, and isolation.

• Threat Modeling & Risk Assessment

• Perform AI/ML threat modeling for new and existing systems, including prompt injection, data poisoning, model extraction, data leakage, and abuse/misuse scenarios.

• Map risks to industry frameworks (e.g., OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF) and drive mitigations with engineering teams.

• Secure MLOps / MLSecOps

• Partner with DevSecOps/MLOps to integrate security into AI delivery pipelines (secure model registry, artifact signing, provenance, access control, dependency scanning, secrets management, CI/CD guardrails).

• Ensure training and inference environments are hardened (cloud IAM, network segmentation, key management, container security).

• AI Security Testing & Red Teaming

• Build and execute AI security test plans and adversarial evaluations (prompt injection, jailbreaks, data exfiltration, content policy bypass, model evasion).

• Develop automated test harnesses and regression suites to validate controls over time.

• Monitoring, Detection & Incident Response

• Define and implement telemetry for AI systems (prompt/output logging, tool calls, policy decisions) with appropriate privacy controls.

• Integrate AI security signals into SIEM/SOC workflows; create detection logic and response playbooks for AI-specific incidents.

• Governance, Privacy & Third-Party Risk

• Support AI governance by defining security requirements for AI use cases, third-party models/vendors, and data usage.
• Partner w…