Lead AI Security Engineer-APS

About the Role

We are seeking a Lead AI Security Engineer to drive the security of next-generation AI systems, GenAI platforms, LLM-powered applications, and AI-enabled enterprise solutions. The role combines Artificial Intelligence, secure software engineering, and offensive application security to identify, assess, and mitigate risks across AI applications and modern software ecosystems.

The ideal candidate is a hands-on AI/software engineer who can build and secure AI systems, apply AI for vulnerability discovery and code analysis, and use offensive security techniques to validate real-world risk. This role is focused on securing AI-led applications and platforms rather than performing traditional infrastructure penetration testing.

Key Responsibilities

AI Security & AI Red Teaming

• Lead AI security assessments for GenAI applications, LLM integrations, AI agents, RAG architectures, and AI-powered enterprise platforms.

• Perform AI red teaming to identify prompt injection, jailbreaks, indirect prompt attacks, data leakage, insecure tool use, agent abuse, RAG poisoning, and model interaction risks.

• Design adversarial test cases and evaluation methods to validate AI system resilience, trust boundaries, and guardrail effectiveness.

• Assess AI application flows, model input/output handling, retrieval layers, plugins/tools, orchestration components, and data exposure risks.

• Define practical mitigation patterns for AI risks, including secure prompt design, policy controls, input/output validation, grounding checks, logging, monitoring, and abuse prevention.

AI Engineering & Security Automation

• Build AI-powered security automation for vulnerability discovery, source code analysis, threat analysis, test generation, and remediation acceleration.

• Develop reusable frameworks, evaluation harnesses, guardrail testing utilities, and workflow automation for secure AI development.

• Integrate AI security validations into SDLC, DevSecOps, CI/CD, SAST, DAST, and code review pipelines.

• Experiment with frontier AI models and emerging AI frameworks to create scalable security assessment capabilities.

• Partner with product engineering teams to embed secure AI engineering practices from design through production.

Application Security & Offensive Validation

• Conduct secure design reviews, threat modeling, code reviews, and vulnerability assessments for AI-led applications, APIs, microservices, and cloud-native platforms.

• Use offensive security techniques to validate exploitability, business impact, and remediation effectiveness.

• Apply knowledge of OWASP Top 10, OWASP API Security, CWE, CVE, CVSS, MITRE ATT&CK, and application security testing methodologies.

• Use tools such as Burp Suite, OWASP ZAP, Nmap, Wireshark, Metasploit, Nessus, Kali Linux, and relevant AI security testing utilities where applicable.

• Collaborate with engineering and architecture teams to prioritize fixes and strengthen secure coding practices.

Technical Leadership

• Lead AI security initiatives across multiple engineering and cybersecurity stakeholders.

• Define standards, playbooks, testing methodologies, and secure AI development guidelines.

• Mentor engineers and security practitioners on AI security, secure coding, and practical AppSec techniques.

• Contribute to security strategy for responsible, secure, and scalable AI adoption across enterprise systems.