• Embed robust security practices throughout the software and AI development lifecycle (SDLC). • Lead secure design reviews, threat modeling, and risk assessments for AI-driven products, APIs, and backend services. • Partner with engineering and product teams to ensure security, privacy, and compliance by design. • Build and maintain security automation and governance frameworks that integrate seamlessly into development workflows. • Architect and enforce security controls for AI/ML systems, including model training, data pipelines, and inference environments. • Identify and mitigate AI-specific attack vectors such as data poisoning, model inversion, prompt injection, and model theft. • Collaborate with governance and compliance teams to align with ethical AI principles and frameworks like NIST AI RMF and the EU AI Act. • Implement model provenance, integrity, and auditability controls to ensure responsible and secure AI operations. • Partner with DevOps and SRE teams to secure service meshes, container networking, and secrets management. • Drive software supply chain security, including artifact integrity, dependency management, and vulnerability reduction. • Build internal frameworks for continuous assurance and real-time vulnerability management. • Define and maintain reference security architectures for microservices, APIs, and AI-powered systems deployed in the cloud. • Mentor teams on secure coding, containerization best practices, and AI risk management. • Promote a security-first culture through advocacy, documentation, and training. • Represent product security in cross-functional initiatives and leadership discussions.