Ai Cybersecurity

V2 Solutions; Chennai District; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

V2 Solutions seeks a Cybersecurity Engineer Sr. – AI Cybersecurity Specialist to lead secure AI governance across cloud and on-premise environments. You will guide AI adoption by assessing LLM apps, agentic systems, RAG pipelines, and model integrations for security risks and guardrails.

You will design AI incident response frameworks, integrate telemetry into SIEM/XDR/SOAR, and coordinate with Security, IT, and business stakeholders to strengthen AI risk posture and protect data integrity.

Qualifications

• Bachelor’s degree in Computer Science, Cybersecurity, Information Systems, Data Engineering, or equivalent experience; Master’s preferred.
• 5+ years of cybersecurity experience, including 1-3+ years focused on AI/ML security, LLM security, model governance, or agentic AI systems.
• Hands-on experience with AI security controls, LLM/RAG architecture hardening, adversarial testing, and secure MLOps.

Responsibilities

• Defines policies, standards, and guardrails for GenAI, LLMs, agentic AI, and ML platforms aligned with security frameworks.
• Leads AI security risk assessments addressing poisoning, evasion, prompt risks, data leakage, and supply chain vulnerabilities.
• Establishes governance for model catalogs, sanctioned AI tools, and enterprise approval pathways.
• Designs and operates AI-assisted incident response frameworks and security telemetry integrations.

Skills

AI security

Incident response

Security governance

Threat modeling

Python

Education

Bachelor’s degree in CS/Cybersecurity/IS

Master’s degree preferred

Tools

Azure OpenAI

Azure ML

AWS Bedrock

SageMaker

Job description

JOB SPECIFIC INFORMATION AI Cybersecurity -Incident Response-Cybersecurity Engineer Job Summary: The Cybersecurity Engineer Sr. – AI Cybersecurity Specialist leads the evaluation, implementation, and governance of secure AI practices across cloud and onpremise environments. This role is responsible for guiding strategic AI adoption by assessing LLM applications, agentic systems, RAG pipelines, and model integrations for immediate security risks, establishing enterprise guardrails, and delivering rapid, intelligencedriven impact assessments to ensure responsible and compliant AI deployment. The engineer also designs and orchestrates AIassisted incident response frameworks capable of operating at machine speed to detect, contain, and neutralize evolving threats while maintaining the integrity and continuity of missionessential services. Through strategic coordination with Security, IT, and business stakeholders, this work materially strengthens ability to defend against AIenabled risks and safeguard the confidentiality, integrity, and availability of data and systems.

Job Responsibilities:

AI Security Strategy & Governance

• o Serves as the senior SME for AI security, defining policies, standards, and architectural guardrails for GenAI, LLMs, agentic AI, and ML platforms aligned with NIST AI RMF, CIS Controls, and security policy.
• o Leads AI security risk assessments, evaluating adversarial ML threats (poisoning, evasion), promptbased risks, output safety concerns, model theft, data leakage, and supplychain vulnerabilities.
• o Establishes governance for model catalogs, sanctioned AI tools, shadowAI detection, and enterprise approval pathways.
• o Embeds securebydesign principles across AI development, testing, deployment, and monitoring pipelines.

LLM / Agentic System Security & Hardening: AI Security Strategy & Governance

• o Serves as the senior SME for AI security, defining policies, standards, and architectural guardrails for GenAI, LLMs, agentic AI, and ML platforms aligned with NIST AI RMF, CIS Controls, and security policy.
• o Leads AI security risk assessments, evaluating adversarial ML threats (poisoning, evasion), promptbased risks, output safety concerns, model theft, data leakage, and supplychain vulnerabilities.
• o Establishes governance for model catalogs, sanctioned AI tools, shadowAI detection, and enterprise approval pathways.
• o Embeds securebydesign principles across AI development, testing, deployment, and monitoring pipelines.

LLM / Agentic System Security & Hardening

• o Develops and validates secure configurations for LLM and RAG architectures, including retrieval permissioning, functioncalling safeguards, and agent workflow boundaries.
• o Conducts AI redteam exercises and adversarial testing; drives guardrail tuning, jailbreak prevention, and output safety assurance.
• o Integrates AI security telemetry into SIEM/XDR/SOAR to improve detection of misuse, exfiltration attempts, or integrity failures.

AI Incident Response & Defense Automation

• o Designs and operates AIassisted incident response frameworks that detect, isolate, and contain threats in real time.
• o Builds automated triage, containment, and recovery agents that preserve missionessential services and enable graceful degradation under attack.
• o Conducts integrity verification, evidence automation, forensic correlation, and postincident AI behavior analysis.
• o Leads crossteam coordination during AIrelated incidents, integrating intelligence, engineering, and operational support.

Automation, Integration & Evidence Management

• o Develops automation pipelines for security policy enforcement, model telemetry ingestion, anomaly detection, and AI governance workflows.
• o Integrates AI controls and evidence collection into ServiceNow GRC/SecOps for continuous monitoring, control testing, and audit readiness.
• o Creates dashboards that visualize AI risk posture, compliance adherence, and control performance.

Training, Awareness & Collaboration

• o Provides training to engineers, developers, and business units on secure AI usage, responsible model interaction, and emerging adversarial threats.
• o Partners with Information Security, Cloud Architecture, SOC/IR, GRC, and business stakeholders to embed AI security controls and align strategies.
• o Monitors threat intelligence related to AI exploitation, synthetic attacks, and industry developments, translating insights into actionable controls.

Requirements, Education, Experience:

Bachelor’s degree in Computer Science, Cybersecurity, Information Systems, Data Engineering, or equivalent experience; Master’s preferred. 5+ years of cybersecurity experience, including 1-3+ years focused on AI/ML security, LLM security, model governance, or agentic AI systems. Hands-on experience with AI security controls, LLM/RAG architecture hardening, adversarial testing, and secure MLOps. Experience with Azure OpenAI, Azure ML, AWS Bedrock/SageMaker, and hybrid AI deployments. Strong understanding of authentication, authorization, access governance, and Zero Trust identity controls related to AI workflows. Proficiency with Python, PowerShell, APIs, and automation tooling for policy enforcement and telemetry processing. Experience with NIST AI RMF, CIS Controls, ISO 27001, SOX, GLBA, FFIEC, and emerging AI regulatory guidance. Strong analytical, communication, and crossteam leadership skills.

Highly Preferred Certifications

• o CISSP, CCSP
• o SANS GSEC
• o Azure AI Engineer Associate
• o AWS Machine Learning – Specialty
• o Google Professional Machine Learning Engineer
• o Additional cloud, governance, or AIfocused certifications are beneficial.