: /n /n Reference Architectures: Design and enforce target-state security architectures for cloud-native, on-premise, and SaaS-based AI/ML deployments, including LLM integration patterns, retrieval-augmented generation (RAG) pipelines, and agentic workflows. /n /n Adversarial Defense: Establish security controls against AI-specific attack vectors, including prompt injection, jailbreaking, training data poisoning, model inversion/extraction, membership inference, and supply-chain vulnerabilities in open-source models/libraries. /n /n Data Security & Privacy: Design zero-trust data boundaries, differential privacy techniques, and strict identity & access management (IAM) controls for training datasets, vector databases, and inferencing endpoints. /n /n MLOps/LLMOps Guardrails: Embed automated security testing (red teaming, static/energetic analysis for ML code, output validation, shadow AI discovery) directly into CI/CD and MLOps pipelines /n /n People Management: Direct, mentor,



and build a high-performing team of AI Security Engineers and Security Architects. /n /n Strategic Roadmap: Define the enterprise AI Security strategy, establishing metrics (KPIs/KRIs) to track risk reduction without impeding development velocity. /n /n Framework Mapping: Map internal security controls to established standards including NIST AI RMF , ISO/IEC 42001 , OWASP Top 10 for LLMs , and MITRE ATLAS . /n /n Cross-Functional Collaboration: Partner closely with Data Science, ML Engineering, Enterprise Architecture, Legal, and Compliance teams to operationalize Responsible AI and privacy requirements (e.g., EU AI Act, GDPR). /n /n Executive Advisory: Serve as the subject matter expert on emerging AI risks for the CISO, CTO, and executive risk boards. /n /n Experience : 11-16 years of experience.

📌 Product Security Architect (Bengaluru)
🏢 Flipkart
📍 Bengaluru