Lead Security Engineer - Penetration Testing & AI Security
About Us
HighLevel is an AI powered, all-in-one white-label sales & marketing platform that empowers agencies, entrepreneurs, and businesses to elevate their digital presence and drive growth. We are proud to support a global and growing community of over 1 million businesses, comprised of agencies, consultants, and businesses of all sizes and industries. HighLevel empowers users with all the tools needed to capture, nurture, and close new leads into repeat customers. As of mid 2025, HighLevel processes over 4 billion API hits and handles more than 2.5 billion message events every day. Our platform manages over 470 terabytes of data distributed across five databases, operates with a network of over 250 microservices, and supports over 1 million hostnames.
Our People
With over 1,500 team members across 15+ countries, we operate in a global, remote-first environment. We are building more than software; we are building a global community rooted in creativity, collaboration, and impact. We take pride in cultivating a culture where innovation thrives, ideas are celebrated, and people come first, no matter where they call home.
Our Impact
As of mid 2025, our platform powers over 1.5 billion messages, helps generate over 200 million leads, and facilitates over 20 million conversations for the more than 1 million businesses we serve each month. Behind those numbers are real people growing their companies, connecting with customers, and making their mark - and we get to help make that happen.
Learn more about us on our YouTube Channel or Blog Posts
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
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.
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.
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.
Application Security, Secure SDLC & DevSecOps
AI Security Assessment & Adversarial Testing
Reporting, Collaboration & Leadership
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.
Familiarity with OWASP guidance for LLM applications, MITRE ATLAS, NIST AI RMF, and related AI security practices.
Programming or scripting proficiency in Python, Go, JavaScript, Bash, or a similar language.
Strong written and verbal communication skills, with the ability to influence technical and non-technical stakeholders.
Preferred Qualifications
Experience building or scaling Application Security practices within a SaaS or product-led technology organization.
Hands-on experience red teaming LLM applications, RAG systems, AI agents, or AI-enabled products.
Experience developing security automation, internal testing tools, or reusable security guardrails.
Contributions to security research, open-source projects, bug bounty programs, or responsible vulnerability disclosure.
Relevant certifications such as OSCP, OSWE, GWAPT, GIAC, CISSP, or an AI Security credential.