Lead AI Security Engineer-IDP
Confirmed on the employer's own hiring board on Oct 24, 2026. First seen by Alion on Aug 11, 2026. Société Générale scores B on the Alion truth index.
Responsibilities
About the Role-
We are hiring a Lead AI Security Engineer to secure enterprise AI platforms, GenAI applications, LLM-based solutions, AI agents, and AI-enabled services across their full lifecycle. The role will focus primarily on AI security engineering, threat modelling, architecture assurance, secure AI control design, adversarial testing, and practical risk mitigation.
Governance, standards, and risk reporting are part of the role, but they are secondary to hands-on AI security execution and the ability to translate AI-specific threats into secure, scalable, and enterprise-ready technical controls.
Key Responsibilities-
1. AI Security Engineering & Architecture
• Design and validate security controls for AI platforms, GenAI applications, LLM integrations, RAG pipelines, AI agents, model endpoints, and AI-enabled workflows.
• Partner with engineering, platform, cloud, data, application security, and architecture teams to embed AI security requirements into design, build, deployment, and operations.
2. AI Threat Modelling, Testing & Risk Mitigation
• Identify and assess AI-specific threats including prompt injection, jailbreaks, insecure tool use, data leakage, model extraction, poisoning, RAG abuse, agent misuse, and AI supply chain exposure.
• Conduct AI threat modelling, adversarial testing, attack surface reviews, control gap analysis, residual risk documentation, and mitigation planning across platforms such as DATALABS, AI Foundry, Azure Cognitive Services, Databricks, and Snowflake.
3. Secure AI Controls, Monitoring & Compliance Support
• Define and review controls for model access, prompt and response handling, sensitive data protection, identity and access, secure APIs, logging, monitoring, abuse detection, and incident readiness for AI systems.
• Use frameworks such as OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF, Responsible AI, and EU AI Act expectations as supporting references for control alignment and audit readiness.
4. AI Security Governance, Enablement & Operational Excellence
• Support AI cybersecurity standards, secure design patterns, assessment checklists, governance inputs, and awareness material while keeping the primary focus on practical security implementation.
• Improve AI security metrics, risk visibility, control automation, and reusable security guidance in partnership with platform, cyber, architecture, and automation teams.
Required Skills-
Skill Area
Requirement
Experience
10+ years in cybersecurity, security architecture, application security, cloud security, AI/ML security, or enterprise security engineering.
AI Security
Strong hands-on experience securing AI, ML, GenAI, LLM applications, RAG pipelines, AI agents, model APIs, or enterprise AI platforms.
AI Threat Knowledge
Deep understanding of prompt injection, jailbreaks, insecure plugins/tools, model poisoning, data leakage, model exfiltration, RAG security, agent security, AI supply chain risk, AI red teaming, and misuse scenarios.
Frameworks & Standards
Working knowledge of OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, Responsible AI, and AI regulatory expectations to support control alignment and governance activities.
Security Assurance
Experience designing, validating, and operationalizing AI security controls, including secure architecture reviews, threat modelling, adversarial testing, remediation tracking, and risk acceptance support.
Enterprise Security
Strong knowledge of cloud security, application security, API security, data protection, IAM, logging, monitoring, security operations, and enterprise risk management as applied to AI systems.
Stakeholder Management
Ability to work with engineering and senior stakeholders, explain AI security risks clearly, and convert them into practical technical controls and mitigation actions.
Preferred Experience-
• Experience with AI security engineering, AI red teaming, adversarial testing, model robustness evaluation, secure RAG design, agent security, or AI platform security in regulated environments.
• Experience with enterprise AI platforms such as Azure OpenAI, Microsoft AI Foundry, Azure AI Services, Databricks, Snowflake, Microsoft Purview, or Microsoft security technologies.
• Exposure to Responsible AI, model risk management, AI governance programs, Python scripting, automation, or relevant security certifications such as CISSP, CISA, CRISC, GIAC, CCSP, or ISO 27001.
Candidate Profile-
• Hands-on AI security expert who can lead secure architecture reviews, threat modelling, control design, testing, and risk mitigation for enterprise AI systems.
• Comfortable working across cybersecurity, platform engineering, cloud, data, AI, architecture, compliance, risk, automation, and business teams in a complex global environment.
• Demonstrates ownership, clear communication, and the ability to improve AI security maturity while keeping governance practical, lightweight, and aligned to delivery needs.
Profile required
Key Responsibilities-
1. AI Security Engineering & Architecture
• Design and validate security controls for AI platforms, GenAI applications, LLM integrations, RAG pipelines, AI agents, model endpoints, and AI-enabled workflows.
• Partner with engineering, platform, cloud, data, application security, and architecture teams to embed AI security requirements into design, build, deployment, and operations.
2. AI Threat Modelling, Testing & Risk Mitigation
• Identify and assess AI-specific threats including prompt injection, jailbreaks, insecure tool use, data leakage, model extraction, poisoning, RAG abuse, agent misuse, and AI supply chain exposure.
• Conduct AI threat modelling, adversarial testing, attack surface reviews, control gap analysis, residual risk documentation, and mitigation planning across platforms such as DATALABS, AI Foundry, Azure Cognitive Services, Databricks, and Snowflake.
3. Secure AI Controls, Monitoring & Compliance Support
• Define and review controls for model access, prompt and response handling, sensitive data protection, identity and access, secure APIs, logging, monitoring, abuse detection, and incident readiness for AI systems.
• Use frameworks such as OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF, Responsible AI, and EU AI Act expectations as supporting references for control alignment and audit readiness.
4. AI Security Governance, Enablement & Operational Excellence
• Support AI cybersecurity standards, secure design patterns, assessment checklists, governance inputs, and awareness material while keeping the primary focus on practical security implementation.
• Improve AI security metrics, risk visibility, control automation, and reusable security guidance in partnership with platform, cyber, architecture, and automation teams.