Lead Security Engineer - Penetration Testing & AI Security

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.

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.

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.