Lead Application Security Engineer

What you'll do

• 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

Tech stack

Benefits