Zeta Global is an AI-Powered Marketing Cloud that utilizes advanced AI and consumer signals for efficient customer acquisition and retention. The Lead Application Security Engineer will enhance the company's security posture by embedding security practices throughout the software development lifecycle and collaborating with multiple teams to ensure secure application development.

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

Senior Cybersecurity Engineer IAM & Endpoint Security

About this role