Lead Security Engineer - Penetration Testing & AI Security

About the Role

We are looking for a Lead Security Engineer - Application Security AI Security with 8+ years of cybersecurity experience and strong expertise in securing modern applications, APIs, cloud-native services, and AI-enabled systems.

Application Security and AI Security will be the primary focus of this role. You will lead security reviews, threat modeling, application assessments, secure SDLC improvements, and vulnerability management across HighLevel s products.

You will also serve as an AI Security specialist, assessing and adversarially testing LLM applications, AI agents, RAG implementations, and AI integrations. This is a hands-on, Lead-level individual-contributor role working closely with Engineering, Product, Infrastructure, and AI/ML teams.

What You'll Be Doing

Application Security, Secure SDLC DevSecOps

• Lead Application Security initiatives across web, mobile, API, microservices, and cloud-native products.

• Conduct architecture reviews, threat modeling, secure design and code reviews, and hands-on security assessments.

• Identify weaknesses in authentication, authorization, tenant isolation, business logic, data protection, and API security.

• Define practical security standards, requirements, guardrails, and reusable secure engineering patterns.

• Improve security testing across CI/CD pipelines using SAST, DAST, SCA, secret scanning, container scanning, and Infrastructure as Code scanning.

• Drive risk-based vulnerability triage and remediation in partnership with engineering teams.

• Develop security automation and promote secure coding through developer guidance, documentation, and training.

  AI Security Assessment Adversarial Testing

• Lead security reviews of LLM applications, AI agents, RAG architectures, machine learning services, and third-party AI integrations.

• Assess AI architectures, including model APIs, data pipelines, vector stores, prompts, fine-tuning workflows, plugins, and agent tool chains.

• Conduct adversarial testing for prompt injection, jailbreaking, sensitive-data disclosure, system-prompt leakage, output manipulation, insecure tool use, excessive agency, and model abuse.

• Evaluate applicable risks involving data poisoning, model inversion, training-data extraction, adversarial evasion, and model exfiltration.

• Test security controls such as guardrails, input/output filtering, access controls, human approvals, logging, monitoring, and abuse detection.

• Develop repeatable AI security testing methodologies, playbooks, automation, and test cases using tools such as Garak, PyRIT, or similar frameworks.

• Assess security and supply-chain risks associated with third-party models, AI platforms, and AI-enabled SaaS products.

  Reporting, Collaboration Leadership

• Produce clear security reports containing evidence, risk ratings, business impact, and actionable remediation guidance.

• Communicate security risks effectively to developers, architects, product leaders, and executive stakeholders.

• Partner with external consultants, researchers, and bug bounty programs for specialized assessments where required.

• Mentor engineers and help establish a security-conscious engineering culture.

• Stay current with developments in Application Security, AI Security, and adversarial testing.

  What You'll Bring

• 8+ years of cybersecurity experience, with deep hands-on expertise in Application Security, product security, penetration testing, or security engineering.

• Experience conducting threat modeling, architecture reviews, secure code reviews, penetration testing, and vulnerability validation.

• 1-3 years of AI Security experience, with AI/ML security, adversarial testing of AI systems, or applied AI research with a security focus.

• Strong knowledge of web, mobile, API, and cloud-native security, including OWASP guidance and business-logic risks.

• Strong understanding of authentication and authorization technologies, including OAuth 2.0, OIDC, JWT, SAML, and modern access-control models.

• Hands-on DevSecOps experience with CI/CD security automation, SAST, DAST, SCA, secret scanning, containers, and Infrastructure as Code.

• Practical knowledge of Docker, Kubernetes, microservices, and cloud security.

• Demonstrated experience assessing or securing LLM applications, RAG systems, AI agents, machine learning models, or AI-enabled products.

• Understanding of AI threats such as prompt injection, jailbreaking, data leakage, insecure tool use, excessive agency, model misuse, and AI supply-chain risks.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.