AI Security in Engineering Teaching Expert (B2B, Part-time)

About the role

Nebius Academy (powered by TripleTen) partners with forward-thinking companies to help technical teams adopt AI safely and build practical, job-ready skills that drive measurable business impact. We design hands-on learning experiences that help engineers apply AI tools in real-world workflows while understanding the security risks introduced by agentic systems, tool use, delegated permissions, data access, and AI-powered development environments. As we continue expanding our AI Security portfolio, we're looking for an experienced Instructor AI Security in Engineering to deliver highly practical workshops for engineering teams. This role combines deep hands-on expertise in AI and agentic security with technical facilitation, helping experienced engineers understand and mitigate risks related to prompt injection, MCP trust boundaries, permissions, AI supply-chain vulnerabilities, secure AI-generated code, and data governance.

Responsibilities

• Deliver hands-on AI Security workshops for experienced engineering teams, combining concise concept explanations with practical labs on real repositories.
• Demonstrate real-world attack scenarios and mitigations involving agentic AI coding tools, including indirect prompt injection, delegated permissions, autonomous command execution, and workspace trust.
• Guide learners through MCP security practices, including trust boundaries, read/write scoping, least-privilege configurations, and re-approval of changed tool definitions.
• Help engineers secure AI-generated code and pull requests using DevSecOps controls such as secret scanning, SAST, SCA, branch protection, CODEOWNERS, and repository-level security instructions.
• Explain and demonstrate AI-specific supply-chain risks, including hallucinated dependencies, poisoned tool descriptions, and compromised MCP servers.
• Facilitate practical discussions around AI data governance, including sensitive data exposure, credentials, proprietary code, model data flows, retention, and content exclusions.
• Review the client's AI tooling configuration and existing security controls ahead of workshops to tailor examples and exercises to their environment.
• Collaborate with the Nebius Academy team and subject-matter experts to refine workshop materials, labs, and security practices based on learner needs and evolving AI security risks.

Requirements

• Strong hands-on experience in AI Security, with practical understanding of security risks introduced by agentic AI systems and AI-assisted engineering workflows.
• Deep understanding of the agentic AI attack surface, including indirect and persistent prompt injection, delegated authority, blast radius, approval flows, workspace trust, and autonomous command execution.
• Practical knowledge of MCP security and trust boundaries, including read/write scoping, least-privilege configuration, permissions, and tool re-approval.
• Hands-on experience with GitHub Copilot, VS Code, and Python, sufficient to demonstrate realistic attacks and mitigations live.
• Strong understanding of DevSecOps controls for AI-generated code, including secret scanning, SAST, SCA, branch protection, CODEOWNERS, and secure repository practices.