[Remote] AI Security Engineer

Note: The job is a remote job and is open to candidates in USA. InvoiceCloud is a fast-growing fintech leader recognized for its innovative solutions and commitment to reducing digital exclusion. They are seeking a highly skilled AI Security Engineer to support their Cybersecurity, Engineering, and Data Science teams, ensuring that AI systems are secure, resilient, and compliant with business objectives.ResponsibilitiesLeads 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 velocityConducts 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 teamsDefines 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 workflowsDelivers 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 maturityEstablishes 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 standardsDrives 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 organizationInventories 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 transparencyDevelops 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 CISOIntegrates 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 AzureBuilds 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 timeDevelops automated regression test harnesses to continuously validate AI security protections as models, prompts, and dependencies evolve, reducing manual effort and improving coverageEstablishes 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 managementAdvances AI Security Testing & Red Teaming capabilities through adversarial experimentation and multi-dimensional analysis, proactively identifying emerging AI threat patterns before production impactLeverages AI and automation to strengthen testing coverage, automate regression validation, enhance anomaly detection logic, and improve the scalability of AI security monitoring and responseContinuously 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 innovationSkillsBachelor'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 DevSecOps2+ years of experience building or securing AI/ML systems (including LLM-based applications) in production environmentsStrong understanding of AI/ML threats and defenses, including prompt injection, data poisoning, model extraction, model inversion, adversarial inputs, data leakage, and abuse/misuse scenariosExperience integrating security into CI/CD and MLOps pipelinesProficiency with cloud platforms (AWS and Azure), container security, IAM, network segmentation, key management, and secrets managementFamiliarity with industry guidance such as OWASP GenAI/Top 10 for LLM Applications, MITRE ATLAS, and/or NIST AI RMFRelevant certifications such as CISSP, CSSLP, CCSP, Azure Security certifications, or GIAC certificationsBenefitsEmployees may also be eligible for an annual bonus or commission.Some roles may also be eligible for overtime pay.Company OverviewInvoiceCloud provides modern digital payment, customer engagement, and outbound disbursement solutions. It was founded in 2009, and is headquartered in Boston, Massachusetts, US, with a workforce of 201-500 employees. Its website is http://www.invoicecloud.net.Company H1B SponsorshipInvoiceCloud, Inc. has a track record of offering H1B sponsorships, with 3 in 2025, 3 in 2024, 5 in 2023, 4 in 2022, 1 in 2021, 1 in 2020. Please note that this does not guarantee sponsorship for this specific role.