Director, Security Engineering - AI

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Job Details Role Summary

The Director, AI Security will build and lead an India-based AI security engineering and enablement function responsible for protecting enterprise use of artificial intelligence: generative AI, copilots, agents, large language models, machine learning platforms, AI-enabled SaaS, and AI-integrated business workflows.

Reporting to the U.S.-based Senior Director, Data & AI Security, with a dotted line to the India-based Senior Director, Endpoint & Infrastructure Security, this role translates global AI security strategy into scalable engineering, control, and governance outcomes. It is both strategic and technical, and focused on securing AI adoption without blocking innovation while addressing data leakage, prompt injection, model misuse, insecure integrations, shadow AI, agentic workflow risk, and intellectual property exposure.

The role establishes India as a strategic global center of excellence for AI security engineering and operations, not only a delivery location.

Key ResponsibilitiesAI Security Strategy and Execution

Translate enterprise Data & AI Security strategy into India-based delivery plans, engineering roadmaps, and measurable objectives.

Lead design and execution of security controls across internally developed models, third-party AI services, SaaS AI features, copilots, retrieval-augmented generation, agentic workflows, and AI-enabled automation.

Establish practical AI security patterns that enable innovation while protecting sensitive data, regulated information, credentials, and IP.

AI Security Architecture and Engineering

Lead AI security architecture across enterprise AI platforms, cloud AI services, model integrations, data pipelines, and AI-enabled applications.

Create and maintain secure design patterns for generative AI platforms, LLM and model API integrations, RAG systems, vector databases and embeddings, AI agents and tool invocation, prompt and response handling, and human-in-the-loop controls.

Ensure designs address confidentiality, integrity, availability, identity, data protection, privacy, auditability, resilience, and abuse prevention.

AI Risk Management and Control Design

Define and operationalize controls for prompt injection (direct and indirect), sensitive data leakage, model manipulation and abuse, insecure plugin/tool/agent integrations, unauthorized access through RAG or connectors, excessive agency and unsafe automation, shadow AI, insecure model supply chain, and IP exposure.