Lead Security Engineer - Artificial Intelligence

About the role

We are seeking a Lead Security Engineer with deep experience securing enterprise systems, cloud platforms, and agent-based AI development environments at scale. This role is hands-on and execution-focused, responsible for defining guardrails around AI workloads across the full lifecycle—development, deployment, training, and inference.

Responsibilities

• Secure corporate AI capabilities used in enterprise applications
• Establish Best Practices for model implementation, versioning, monitoring and governance for AI Systems on the Enterprise.
• Design/Implement guardrails for AI code-generation tools used in developer workflows
• Enable and implement safe AI-assisted development across IDEs, CI/CD pipelines, and local developer environments
• Support model selection and integrations within the organization (Claude class, GPT-class, and similar platforms)
• Engineer and secure Microsoft Agents, Copilot-style workflows, and agent-driven automation.
• Prevent insecure code generation, prompt leakage, and unsafe agent behavior while preserving developer velocity

Qualifications

• Experience with creating/managing AI Agent IDs and MCP servers and integrations
• Strong proficiency in Python for AI workflows, automation, and orchestration
• Experience with RAG pipelines, embeddings, APIs, and AI service integration
• Understanding of AI lifecycle risks
• Strong experience securing AI workloads on AWS & Azure
• Experience with Cloud Hardening Best Practices
• Strong Infrastructure-as-Code (IaC) for Cloud, preferably Terraform
• Strong background in application security, cloud security, and IAM
• Experience embedding security into CI/CD, IaC, and SDLC workflows
• Automation experience using Python, PowerShell, Bash, and APIs
• Strong RHEL Linux skills, especially at the command line level
• Strong understanding of AI/LLM-specific threats such as prompt injection, data poisoning, model theft, adversarial attacks, sensitive data leakage, etc.
• Experience implementing AI security controls such as guardrails, content filtering, input/output validation, RBAC for AI systems, secure prompt handling, and AI audit logging
• Understanding of secure AI architecture and AI governance frameworks
• Familiarity with: OWASP Top 10 for LLM Applications, NIST AI Risk Management Framework, Responsible AI and AI compliance practices, SIEM, threat detection, and vulnerability management