Lead Application Security Engineer

Zeta Global is an AI-powered marketing cloud company that uses artificial intelligence and consumer data to help marketers acquire, grow, and retain customers. The Lead Application Security Engineer will advance application and platform security through AI-native practices, automated controls, threat modeling, vulnerability management, and secure software development lifecycle integration. The role collaborates with Engineering, Product, QA, DevOps, and AI platform teams to build secure-by-default systems and proactive defense capabilities.

Responsibilities:

• Use AI-assisted threat modeling capabilities to identify application, platform, API, cloud, data, and AI/ML security risks early in the design and development process
• Leverage automated security review tools to evaluate architecture, design documents, code changes, APIs, and data flows for security gaps and control weaknesses
• Drive AI-assisted code security reviews using SAST, DAST, SCA, secrets detection, IaC scanning, container scanning, and contextual risk analysis
• Use automation and intelligent correlation to assess third-party libraries, APIs, vendor integrations, and open-source dependencies for security, compliance, and supply-chain risk
• Support AI-enabled red team, blue team, and incident response simulations to validate detection, prevention, and response capabilities
• Partner with developers and QA engineers to embed AI-driven security testing and automated risk detection into CI/CD pipelines
• Build and improve security automation that provides real-time feedback to developers during design, coding, testing, release, and deployment
• Use AI-assisted analysis to review architecture and design artifacts, identify risks earlier, and recommend secure implementation patterns
• Contribute to intelligent security checkpoints that reduce manual review effort while improving consistency, traceability, and developer velocity
• Help design scalable guardrails, reusable security controls, and policy-as-code capabilities across application and platform teams
• Monitor evolving application, cloud, API, AI/ML, and data security risks using AI-assisted threat intelligence, vulnerability intelligence, and attack-pattern analysis
• Identify and evaluate AI-specific threats such as prompt injection, data poisoning, model abuse, model leakage, insecure tool use, and sensitive data exposure
• Assist in designing and deploying proactive defense mechanisms across applications, APIs, data platforms, and AI-powered systems
• Use automated signals, telemetry, and risk scoring to support investigations, post-incident analysis, and continuous improvement of prevention and detection capabilities
• Translate recurring vulnerabilities and incidents into feedback loops that improve threat models, secure design patterns, and SDLC controls
• Promote secure coding and secure design practices through AI-assisted guidance, reusable playbooks, automated recommendations, and developer-friendly documentation
• Contribute to internal security standards, secure engineering patterns, and AI-native security playbooks
• Help teams adopt security self-service capabilities that reduce dependency on manual AppSec review
• Collaborate closely with Engineering, DevOps, QA, Product, and AI platform teams to foster a security-first and automation-first culture
• Use metrics and insights to measure control effectiveness, remediation trends, developer adoption, and overall security maturity

Requirements:

• Bachelor's degree in Computer Science, Cybersecurity, or a related field, or equivalent practical experience
• 5+ years of experience in Application Security, DevSecOps, Secure Software Development, or Security Engineering
• Strong understanding of OWASP Top 10, SANS CWE Top 25, secure design principles, and application threat modeling
• Familiarity with AI/ML security concepts such as prompt injection, data poisoning, adversarial testing, model integrity, model abuse, and AI supply-chain risks
• Experience building or integrating AI-assisted security workflows, security bots, automated triage systems, or risk scoring models
• Experience using AI-assisted or automation-driven approaches to improve security testing, vulnerability analysis, code review, or risk prioritization
• Experience with modern application frameworks and architectures such as React, Node.js, Django, FastAPI, or similar technologies
• Knowledge of securing APIs, microservices, authentication, and authorization mechanisms such as OAuth2, OIDC, JWT, and service-to-service authentication
• Experience with cloud platforms such as AWS, GCP, or Azure, and containerized environments such as Docker and Kubernetes
• Working knowledge of security testing and automation tools such as Semgrep, SonarQube, Burp Suite, OWASP ZAP, Trivy, Snyk, GitHub Advanced Security, or similar tools
• Ability to analyze security findings, correlate risk context, and drive practical remediation guidance for engineering teams
• Strong collaboration and communication skills with the ability to work across Engineering, Product, QA, DevOps, and Security teams
• Experience with policy-as-code, infrastructure-as-code security, CI/CD security controls, and automated governance
• Experience with automation frameworks and scripting for security testing, vulnerability validation, and remediation workflows
• Relevant certifications such as OSCP, GWAPT, CSSLP, cloud security certifications, or AI/ML-specific security certifications