AI Security Lead

Responsibilities:

• Define and own the enterprise AI security strategy covering LLMs, ML models, agentic systems, and AI infrastructure.
• Conduct AI threat modelling: adversarial attacks, model inversion, data poisoning, prompt injection, and model extraction.
• Secure AI training and inference infrastructure: model serving hardening, API security, and pipeline integrity.
• Lead AI red-team initiatives: systematic adversarial testing of LLM and agentic systems in production.
• Build AI governance frameworks: model risk management, provenance controls, audit trails, and approval workflows.
• Secure agentic systems and orchestration layers: tool-use security, agent permission models, and sandboxing strategies.
• Define model integrity and provenance controls: signing, versioning, reproducibility, and tamper detection.
• Conduct AI security assessments and vulnerability reviews across all production AI systems before and after deployment.
• Establish secure AI deployment practices, ensuring platform security across cloud infrastructure, containers, and API layers.
• Implement privacy-preserving architectures (data masking, synthetic data, differential privacy) to safeguard corporate PII from leaking into LLM training sets or context windows.
• Embed automated security compliance scanners, vulnerability checks, and input/output guardrails directly into the MLOps CI/CD pipelines.

Requirements:

• Strong experience securing AI, ML, and LLM-based applications in production environments.
• Deep understanding of AI threat modeling, prompt injection, jailbreaks, data poisoning, and adversarial attacks.
• Strong knowledge of OWASP LLM Top 10 and AI security best practices.
• Experience conducting AI security assessments, red teaming, and vulnerability reviews.
• Understanding of model governance and secure AI deployment.
• Strong hands-on cybersecurity experience across cloud, application, and platform security, including penetration testing, vulnerability management, SIEM/SOC experience, or security architecture at the infrastructure layer.
• AI ecosystem knowledge is a must. Conceptual clarity on transformer architectures, fine-tuning pipelines (SFT/RLHF), and vector search mechanics.