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Niche MLSecOps specialization reduces pool, but strong employer brand and broad 4–12 year range raise medium competition.
Role demands MLSecOps and offensive AI security expertise, limiting cross-industry transferability.
Explicit years, MLSecOps, offensive security and published research requirements make screening highly strict.
Lead AI-specific offensive security efforts for o9’s GenAI platform and agentic AI ecosystem, focusing on emerging threats and attack surfaces.
Design and run AI-specific VAPT engagements, red team exercises, and scenario-based tabletop simulations with actionable remediation based on CVSS and MITRE ATLAS frameworks.
Develop AI security automation tools and contribute to threat modeling of autonomous agent clusters while integrating red team findings to improve detection capabilities across 500+ customer environments.
4-12+ years of experience in application security, AI/ML security, or offensive security research with at least one demonstrable AI-specific assessment or published finding.
Hands-on offensive expertise in LLM attack surfaces including jailbreaks, multi-modal injection, agentic control gaps, and indirect prompt injection.
Working knowledge of MLOps pipelines, ML engineering concepts, or cloud security.
Work Experience Required: 4-12+ years as mentioned; Notice Period: Not explicitly mentioned in the JD.
Experienced in AI offensive security research with demonstrable AI-specific vulnerability assessments or published work indicating domain expertise.
Skilled in operating at the intersection of AI/ML, security engineering, and offensive research with strategic focus on securing autonomous agents and generative AI platforms.
Comfortable driving structured security engagements and integrating findings into SOC and detection engineering in complex, large-scale environments (500+ customers, cloud, Kubernetes).