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. You will operate hands-on across high-visibility initiatives, embedding security, trust, and resilience into AI systems while enabling rapid, responsible innovation.

Key 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. Manage AI defensive technologies and their operations.

• 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.

Qualifications

• Required:

  • 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

• Preferred:

  • 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