Sr Product Security Engineer, AI & DevSecOps

Job summary

The Senior Product Security Engineer, AI & DevSecOps role focuses on embedding security throughout the software development lifecycle, particularly for AI-enabled products and cloud-native platforms. Key responsibilities include conducting security architecture reviews, implementing security controls in CI/CD pipelines, and securing AI systems, while utilizing technologies such as Kubernetes, Terraform, and various security tools.

Qualifications

• Typically requires 7+ years of relevant experience in application security, product security, security engineering, or a related cybersecurity discipline
• Experience conducting security architecture reviews, threat modeling, secure design reviews, and vulnerability remediation
• Experience implementing DevSecOps practices and integrating security controls into CI/CD pipelines
• Experience securing AI/ML platforms, generative AI solutions, large language model applications, or data science workflows
• Experience with secure software development lifecycle practices, vulnerability management, and Agile development methodologies
• Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform and cloud-native technologies including containers and Kubernetes
• Experience with infrastructure-as-code and CI/CD technologies such as Terraform, GitHub Actions, Azure DevOps, GitLab, Jenkins, or similar tools
• Proficiency in scripting and automation using Python, PowerShell, Bash, or comparable languages
• Experience with security tooling including SAST, DAST, software composition analysis (SCA), container security, secrets detection, and infrastructure scanning

Responsibilities

• Conduct security architecture reviews, threat modeling, and security assessments for applications, APIs, cloud services, and AI-enabled systems
• Define and implement security requirements, standards, reusable design patterns, and security guardrails across the software development lifecycle
• Identify, assess, and help mitigate security risks while partnering with engineering teams to remediate vulnerabilities and strengthen secure coding practices
• Assess and secure AI, machine learning, and generative AI solutions, including controls for model governance, secure access, data protection, and AI risk management
• Design and implement security controls for cloud-native applications, containers, Kubernetes, serverless platforms, and infrastructure-as-code deployments
• Integrate security controls and automated testing into CI/CD pipelines, including SAST, DAST, software composition analysis, secrets detection, container scanning, and infrastructure scanning
• Develop security automation using scripting, infrastructure-as-code, policy-as-code, and compliance-as-code technologies
• Support identity and access management, secrets management, cloud security monitoring, vulnerability management, and incident response activities
• Contribute to compliance initiatives, security metrics, risk reporting, and continuous improvement efforts
• Provide technical guidance on secure development practices and champion a security-first engineering culture

Skills

Degrees

Relocation