Domain Manager - Cyber Risk Unit

Job Summary

Identify and exploit vulnerabilities in AI models and systems through realistic adversarial simulations to prevent exploitation by malicious actors.

Evaluate models for security weaknesses and risks like supply chain, AIBOM vulnerabilities across GenAI Agentic platform.

Hands on experience on critical attack techniques including prompt injection, jailbreaking, data poisoning, model inversion, and good understanding on OWASP LLM Top 10, OWASP Agentic Top 10, MITRE ATLAS framework.

Plan and conduct targeted red team engagements across large language models, generative AI applications, and supporting ML infrastructure.

Produce detailed, actionable reports with clear attack reproductions, impact assessments, and recommended hardening measures.

Good understanding on Guardrail implementation, Rule engine, Policy configuration and AI Runtime security.

Safeguard live AI inference environments and deployed models against active runtime threats including model extraction, evasion, backdoor activation, and prompt-based attacks.

Implement layered runtime defenses such as input/output sanitization, anomaly detection, rate limiting, and content scanning tailored to AI workloads.

Perform threat modeling and risk analysis tailored to agentic architectures, focusing on risks such as goal misalignment, tool misuse, and privilege escalation.

Work closely with MLOps, platform, and SRE teams to embed runtime protection practices into deployment pipelines and operational processes.

Lead incident response for AI-specific security events, performing root-cause analysis, containment, and remediation with minimal service disruption.

Assess Third Party Partner vulnerabilities and security risk

Create and maintain specialized tooling, test harnesses, and automation to enable efficient, repeatable adversarial security testing for AI systems.

Regular cadence with AI Development team for secure design, suggest architectural reviews.

Connect with AI

Duties and Responsibilities Minimum Accountabilities

Engineering / Computer Graduate with 4-6 years of Information / Cyber Security Experience and minimum 1-2 years of hands-on experience on AI Red Teaming, AI Guardrails implementation will be preferred.

Additional Accountabilities

AI securityrelated certifications (Good to Have)

Key Decisions / Dimensions

Involvement in Activity calendar planning and execution of AI applications

Preparation of testcases for AI Red Teaming and highlight the risk imposing of the vulnerabilities

Major Challenges

AI Red Teaming understanding, execution, reporting timely closure from stakeholders

Change management and AI sign off with in defined TAT

Required Qualifications and Experience Qualifications

Post-Graduates with relevant security experience of 4-6years (also graduates with experience of 6-8 years may apply)

Work Experience

Engineering / Computer Graduate with 4-6 years of Information / Cyber Security Experience

Understanding of OWASP LLM Top 10, OWASP Agentic Top 10, MITRE ATLAS framework.

Common AI-specific threats: Prompt injection (direct indirect), Model poisoning data poisoning, Model theft / extraction, Hallucination abuse unsafe outputs

Threat modeling for AI systems (LLMs, RAG, agents)

Strong understanding of application security (OWASP Top 10, API Security Top 10)

Secure SDLC practices (design reviews, threat modelling, secure coding)