Own our multi-modal AI stack from OSINT agents to voice agents delivering realistic social engineering attacks
Build MCP servers to enhance agent capabilities and architect agentic memory layers for optimal context delivery
Develop agent evaluation frameworks and fine-tune SLMs to optimize social engineering campaign outputs
Deploy production AI agents using vector and graph databases, LLM observability stacks, and modern orchestration tools
Research and publish on multi-agent systems best practices
A bit of everything.. Plain software engineering, data, etc.

Healthy skepticism of AI-generated code and proficiency in getting the most out of AI-assisted coding while remaining a critical thinker
Strong Engineering fundamentals, this role is for someone that can work autonomously across the stack with a heavy lean towards applied AI
Deep expertise in Python and Go for building scalable backend systems
Strong experience with vector databases, graph databases, context engineering, and agentic memory/orchestration
Hands-on experience operating and leveraging LLM observability stacks
Proficiency with containerization (Docker) and orchestration (Kubernetes, AWS)
Experience with major cloud providers (AWS) and CI/CD best practices
Commitment to writing high-quality, maintainable, and well-tested code

Define the next generation of AI-native security validation at enterprise scale
Protect hundreds of thousands of employees at Fortune 500 companies from social engineering attacks
Work at the cutting edge of multi-agent systems and security research
Own entire AI features from research to production with no bureaucracy
Shape engineering culture and practices as we grow

GhostEye

Founding Engineer - Applied AI