Sr. AI Security Engineer
Location: Plano, TX (Onsite from Day 1)
C2C or W2
Overview:
Drive the development and optimization of advanced AI architectures (cloud/hybrid), software, tools, processes, and workflows to predict, detect, and prevent AI specific threats. This hands-on technical leadership role combines deep research in AI security with close collaboration across teams to integrate AI capabilities into robust cybersecurity architectures.
Key Responsibilities:
Design, develop, and optimize AI/AI Security specific architectures, software, threat models, tools, solutions for threat identification, prediction, and prevention.
Implement and secure agentic and MCP architectures, neural networks, and AI techniques to enhance threat detection, monitoring, and risk scoring.
Integrate AI security tools and technologies across AI architectures, collaborating with data scientists, security engineers, and other stakeholders.
Analyze AI security risk scoring and incident data to refine and improve AI architectures and methodologies.
Provide technical leadership and mentorship to junior engineers in Agentic and MCP architectures
Ensure alignment and compliance with industry standards (NIST AI-RMF, ISO42001, OWASP Top 10 for LLM), and advanced security architectures (Agentic, MCP).
Stay abreast of emerging trends and advancements in AI and cybersecurity.
Required Qualifications:
A bachelor's or master's degree in computer science, Engineering, or a closely related discipline is required.
5+ years of experience in AI and cloud-focused architectures and cybersecurity in an enterprise environment.
8+ years deep hands-on low/high level software development expertise in Python, R, Java, C or similar programming languages.
Deep hands-on understanding of agentic frameworks (LangGraph, LangChain, Crew, OpenClaw etc.,), and AI orchestration.
Hands-on experience with AI security technologies (intrusion detection, anomaly detection, threat intelligence).
5+ years' experience in Azure or AWS cloud native services, architectures, and tools.
Expertise in enterprise architecture (including Cloud-native and AI architecture patterns).
Advanced knowledge of security and governance frameworks (NIST AI-RMF, ISO42001, OWASP Top 10 for LLM).
Strong communication and collaboration skills.
Preferred Qualifications:
Experience with agentic and model context protocols (MCP) architectures.
Demonstrated ability to lead cross-functional technical teams.
Track record of published research or thought leadership in AI security.
