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Security Engineer
Belmont Lavan. Secure autonomous AI agents before and after production deployment .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in security engineering with a focus on securing LLM and agent-based systems, including hands-on knowledge of attack techniques and identity access controls. Proficient in cloud security practices across major platforms and capable of effectively communicating AI risks to diverse stakeholders.
Highest-signal resume keywords
Security EngineeringLLM Attack TechniquesIdentity And Access ControlsCloud SecurityPython Programming
ATS Keywords
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Hard Skills
Security EngineeringLLM Attack TechniquesIdentity And Access ControlsCloud SecurityPython ProgrammingGo ProgrammingTypeScript ProgrammingRust ProgrammingGovernance ControlsAgent Frameworks
Soft Skills
Communication Skills
Tools & Technologies
OAuth 2.0OIDCAWSAzureGCPOPACedarLangGraphOpenAI Agents SDKCrewAI
Industry Keywords
AppSecCloud SecurityIAMOffensive SecurityAgent-Based Systems
Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformPythonRustTypeScriptGo
About the role
Key responsibilities & impact- Secure autonomous AI agents before and after production deployment
- Design guardrails for AI agents
- Attack agent systems to identify security weaknesses
- Build governance controls enabling confident agent deployment
- Work between AI engineering and security teams
Requirements
What you’ll need- 5+ years in security engineering (AppSec, cloud security, IAM or offensive security), with at least 1 year securing LLM or agent-based systems
- Hands-on knowledge of LLM attack techniques: prompt injection, jailbreaks, data exfiltration via tools, RAG poisoning
- Experience designing identity and access controls: OAuth 2.0 / OIDC, token exchange, workload identity, secrets management
- Working knowledge of agent frameworks and protocols such as LangGraph, OpenAI Agents SDK, CrewAI or Model Context Protocol (MCP)
- Strong coding skills in Python, plus one of Go, TypeScript or Rust
- Cloud security on AWS, Azure or GCP, including logging, detection and policy-as-code (e.g. OPA / Cedar)
- Ability to explain AI risk clearly to engineers, risk teams and executives