AI Security Engineer

• Design, develop, and deploy autonomous for enterprise-scale business workflows.
• Own the end-to-end AI agent lifecycle, including architecture, implementation, testing, deployment, monitoring, and optimization.
• Make architectural decisions that support scalability, reliability, performance, and security.
• Optimize LLM token consumption, inference costs, response latency, and overall agent efficiency.
• Implement security controls, guardrails, validation mechanisms, and testing frameworks for AI-powered applications.
• Apply Security Harness Engineering principles to improve the security, reliability, and resilience of AI systems.
• Integrate security practices into AI development workflows, including application security, DevSecOps, code analysis, and threat modeling.
• Identify and mitigate security risks associated with autonomous agents, LLM applications, and AI-enabled software systems.
• Apply secure-by-design and secure-by-default principles throughout the software development lifecycle.
• Collaborate with engineering, security, and business teams to translate requirements into production-ready solutions.
• Communicate architecture decisions, technical trade-offs, and business outcomes to technical and non-technical stakeholders.

We are seeking an experienced AI Security Engineer with strong hands-on expertise in designing, developing, and deploying autonomous AI agents for enterprise-scale business workflows. The ideal candidate will combine advanced AI engineering capabilities with a strong understanding of application security, secure software development, and emerging AI security practices.

The candidate must have experience taking AI agents from architecture and development through production deployment, with the ability to explain technical decisions, demonstrate end-to-end ownership, and deliver measurable business outcomes., * Proven hands-on experience building and deploying autonomous AI agents into production within enterprise environments.

• Strong understanding of AI agent architectures, LLM-based applications, and agentic AI workflows.
• Experience owning AI solutions end to end, from design and development to production implementation.
• Demonstrated experience optimizing LLM token usage, inference performance, and operational costs.
• Strong understanding of security engineering principles and security controls for AI-powered systems.
• Ability to articulate system architecture, design decisions, implementation challenges, and measurable business results.
• Experience developing secure, scalable, and reliable production software solutions.

Preferred Qualifications

• Experience applying AI within security domains, including:

• Application Security (AppSec)
• DevSecOps and secure code analysis
• Threat modeling and vulnerability detection
• Firmware security
• Software supply-chain security