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Niche AI security skillset reduces applicant pool despite Bangalore metro location.
Highly transferable security engineering skills but AI/regulated finance context increases domain specificity.
Explicit 7+ years, mandatory security/cloud experience, and domain-specific regulatory knowledge increase filter strictness.
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Own security for integration and adoption of third-party AI and large language model (LLM) services across the enterprise, focusing on API security, data exposure controls, and vendor risk assessment.
Develop and enforce AI security governance frameworks aligned with regulatory requirements (GLBA, PCI DSS, DORA, NYDFS) and emerging AI security standards (NIST AI RMF, ISO/IEC 42001).
Identify and mitigate AI-specific risks such as prompt injection and data leakage, and partner with security operations to build detection and response capabilities for AI-enabled systems.
Bachelor's degree in Computer Science, Information Security, Engineering, or related technical field or equivalent experience.
7+ years of experience in security engineering, application security, or cloud security.
Hands-on experience securing cloud-native environments and API integrations (AWS, Azure, or GCP).
Demonstrated knowledge of AI/ML security risks in enterprise contexts and practical engagement with AI security (certifications, applied work, or research).
Experienced security engineer with demonstrated focus on AI/ML security risks and secure integration patterns for third-party AI services.
Familiar with regulatory compliance and governance frameworks related to AI security, including GLBA, PCI DSS, NYDFS, DORA, and emerging standards such as NIST AI RMF and ISO/IEC 42001.
Ability to translate complex AI security risks into actionable technical and governance controls, and communicate effectively with both technical teams and leadership.