AI Security Engineering Lead

Key Responsibilities

• Define and enhance security architecture for AI/ML systems, including data pipelines, model development, and deployment environments
• Design and implement security controls such as model validation, guardrails, monitoring, and risk management frameworks
• Partner with engineering, data, and business teams to integrate secure-by-design principles into AI solutions
• Evaluate and implement enterprise AI platforms and tools through proof-of-concept and production deployments
• Establish scalable operating models, standards, and best practices for AI security
• Assess emerging technologies (e.g. generative AI, agent-based systems) and their security implications
• Provide technical leadership and guidance on AI security risks, controls, and governance

Requirements

• Minimum 8-12 years of experience in cybersecurity, software engineering, or platform engineering, with exposure to AI/ML environments.
• Hands-on experience with AI/ML platforms (e.g. Databricks, AWS SageMaker, Azure ML) and secure data pipelines.
• Familiarity with cloud platforms (e.g. AWS, Azure, GCP) and AI/ML services.
• Understanding of AI security concepts such as model risk, adversarial threats, and secure deployment practices.
• Proficiency in programming/scripting (e.g. Python, Java) and working with APIs and data pipelines.
• Strong knowledge of security frameworks and best practices.
• Strong analytical, communication, and stakeholder management skills.

Morgan Mckinley Pte Ltd

EA Licence No: 11C5502

EAP Registration No: R1106192