AI Security Engineer Manager
About the role
This role plays a critical role in securing the development and deployment of AI/ML and Generative AI solutions. The position requires a collaborative, execution-focused leader who can influence teams, mentor engineers, and ensure AI systems meet enterprise security, risk, and compliance expectations.
Responsibilities
Secure, Outcome-Driven Delivery: Design and deliver AI-enabled solutions that are secure by design, balancing business value with risk mitigation. Solve complex problems while ensuring protection of data, models, and systems.
Hands-On AI Security Engineering: Actively contribute to architecture, design, and development of AI/ML and GenAI systems with embedded security controls. Integrate security across the SSDLC, including code reviews, testing, and deployment.
AI Risk Identification and Mitigation: Identify and address AI-specific vulnerabilities, including prompt injection, data leakage, model manipulation, and misuse. Implement practical safeguards to ensure system integrity and trustworthiness.
Technical Leadership and Advocacy: Serve as a trusted technical voice for secure AI engineering. Ensure solutions are feasible, secure, and aligned with business and customer objectives.
Engineering Excellence with Security Focus: Maintain high standards for code quality, scalability, and security. Contribute to secure coding practices, reusable patterns, and continuous improvement across engineering teams.
Iterative and Responsible Innovation: Support rapid experimentation while applying appropriate security guardrails. Enable teams to innovate safely through controlled, risk-aware development practices.
Cross-Functional Collaboration: Partner closely with product, engineering, cybersecurity, and risk teams to embed AI security into solutions. Balance usability, performance, and security in decision-making.
Standards and Best Practices: Apply and help evolve standards for AI security, including data protection, access control, model validation, and monitoring within DevSecOps and MLOps pipelines.
Communication and Influence: Clearly articulate technical risks, trade-offs, and solutions to both technical and non-technical stakeholders. Contribute to alignment and informed decision-making.
Requirements
Ability to work independently and collaborate as part of a team
Effective written and verbal communication skills
Meticulous attention to detail and quality of work product
Ability to build and sustain professional relationships
Ability to lead projects or workstreams
Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
Strong interpersonal skills and professional demeanor
Ability to meet deadlines
Ability to mentor and provide clear guidance to others
Qualifications
Bachelor’s degree or equivalent in Computer Science, Computer Engineering, Business Administration
Minimum 6 years of relevant experience in software engineering, cybersecurity, and/or including AI/ML, with hands-on delivery experience
Minimum 1 year of people and/or process management experience
Strong understanding of AI/GenAI technologies and associated security risks (e.g., prompt injection, data exposure, adversarial threats)
Experience building and securing applications using Python, JavaScript, or similar, along with ML frameworks (e.g., PyTorch, TensorFlow)
Familiarity with secure development practices (DevSecOps) and integrating security into CI/CD and MLOps pipelines
Experience with cloud platforms (AWS, Azure, GCP) and cloud-native security principles
Knowledge of data protection, identity/access management, and secure architecture patterns
Ability to work across teams, mentor engineers, and contribute to a strong engineering culture
Strong communication skills with the ability to translate technical concepts into business-relevant insights