AI Security Full Stack Engineering Manager

• Design, build, and operate security capabilities for AI and generative AI platforms.
• Develop services, APIs, and workflows that automate AI risk detection, policy enforcement, and compliance validation.
• Integrate enterprise AI security technologies and frameworks into developer and platform engineering workflows.
• Support secure AI operations across model development, deployment, and monitoring environments.

• Design and develop secure web applications, dashboards, APIs, and backend services.
• Build security controls into frontend, backend, and API layers.
• Implement authentication, authorization, and access control solutions using modern identity standards (OAuth 2.0, OpenID Connect, JWT).
• Develop reusable services, SDKs, and APIs to enable secure-by-design adoption across engineering teams.

• Design and implement secure cloud-native architectures across Azure, Google Cloud Platform (GCP), and Amazon Web Services (AWS).
• Build and secure containerized and serverless applications using Kubernetes and cloud-native technologies.
• Implement Infrastructure as Code (IaC) using Terraform and embed security controls into cloud provisioning processes.
• Collaborate with platform teams to improve security posture across AI workloads and supporting infrastructure.

• Develop automation that continuously identifies, prioritizes, and remediates AI security risks.
• Integrate security controls into CI/CD pipelines, including code scanning, infrastructure scanning, secrets detection, and policy validation.
• Build observability solutions that provide logs, metrics, traces, monitoring, and alerting for AI platforms.
• Enable self-service security capabilities that reduce manual operational effort.

• Implement controls to address AI-specific risks such as prompt injection, sensitive data exposure, model misuse, and adversarial attacks.
• Conduct AI threat modeling and security architecture reviews.
• Partner with security operations teams to develop detection, monitoring, and response capabilities for AI systems.
• Evaluate emerging AI security technologies and drive adoption where appropriate.

• Lead, mentor, and develop a high-performing team of AI security engineers.
• Define technical roadmaps, architecture standards, and engineering best practices.
• Prioritize product capabilities based on business impact, risk reduction, and customer needs.
• Drive engineering excellence through design reviews, code reviews, and operational ownership.

• Secure, scalable AI security platform capabilities delivered on roadmap.
• Automated AI risk detection and policy enforcement integrated into engineering workflows.
• Enterprise observability, monitoring, and alerting for AI environments.
• CI/CD-integrated security controls adopted across AI engineering teams.
• Reusable APIs, SDKs, and automation services that enable secure-by-design development.
• A high-performing AI security engineering team delivering measurable business impact.

• Bachelor's degree in Computer Science, Cybersecurity, Engineering, or a related field, or equivalent experience.
• 10+ years of experience in software engineering, cybersecurity, platform engineering, or related disciplines.
• 3+ years of experience securing AI/ML, generative AI, or data-driven platforms.
• Experience leading engineering teams and delivering enterprise-scale technology solutions.

• Strong hands-on development experience with Python and at least one modern backend technology (Node.js, Java, or Go).
• Experience building RESTful APIs and microservices.
• Proficiency with modern frontend frameworks such as React, Angular, or Vue.
• Strong understanding of secure application development practices.

• Experience with Azure, GCP, or AWS.
• Strong understanding of Kubernetes, container security, and cloud-native architectures.
• Experience with Infrastructure as Code, preferably Terraform.
• Familiarity with CI/CD pipelines and Git-based development workflows.

• Deep knowledge of application security principles and OWASP standards.
• Experience with API security, Identity and Access Management (IAM), encryption, and secrets management.
• Strong understanding of threat modeling, security monitoring, and incident response.

• Knowledge of generative AI and large language model (LLM) security risks.
• Experience with prompt injection mitigation, data protection, model governance, and AI threat modeling.
• Familiarity with AI security frameworks, controls, and secure AI deployment practices.

• Experience building security platforms, developer tools, or enterprise security products.
• Experience securing LLM-based applications and AI platforms.
• Familiarity with Microsoft AI security solutions, Google Model Armor, Palo Alto Prisma AIRS, or similar technologies.
• Experience implementing AI security monitoring, governance, and risk management capabilities.
• Professional security or cloud certifications (CISSP, CCSP, AWS Security, Azure Security, GCP Security, or equivalent).

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