AI Security Engineer
Remote · Mumbai, Maharashtra, India
Job Summary
AI Security Engineer role focused on designing, engineering, and operationalizing AI security across enterprise AI-enabled platforms, including AI security architecture, guardrails, identity and access controls for non-human identities, threat modeling, monitoring, and cross-functional security delivery. Responsibilities include building secure-by-design patterns for AI development, deployment, and operations of AI-enabled SaaS applications, internal AI agents, model hosting and orchestration layers, RAG architectures, and training pipelines; implementing authentication, authorization, and least-privilege controls for human and agent identities; identifying AI-specific risks (data leakage, prompt injection, jailbreaks, data poisoning, model extraction, AI supply-chain risk), and ensuring security testing and incident readiness; collaboration with IAM, SecOps, AppSec, GRC, IT engineering, AI platform teams, and business stakeholders; secondary focus on data security with Microsoft Purview, data classification, DLP, and enterprise data protection in AI workflows; remote-friendly with UK business hours alignment and flexibility to work remotely as needed.
Required Qualifications
- 8+ years of experience in cybersecurity engineering, cloud security, application security, or data security
- Direct, hands-on experience with Azure AI Foundry and Copilot Studio in enterprise environments
- Strong experience securing cloud and SaaS platforms (Azure preferred)
- Deep understanding of identity, access control, data protection, and secure application/API design
- Proven ability to translate security requirements into practical, deployable controls
- Experience securing generative AI, LLM-based systems, and agentic architectures
- Experience with Microsoft Copilot Administration, Anthropic and other AI platforms (e.g., OpenAI ecosystems)
- Experience with Microsoft Purview (sensitivity labels/information protection, DLP, Insider Risk Management)
- Familiarity with RAG architectures, vector databases, embeddings, and MCP integrations
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