AI Security Architecture Manager: 6-9 years (Advanced)

Job Description: AI Security Architecture Manager – Agentic AI Vulnerability Detection & Remediation

Role Title: AI Security Architecture Manager
Work Location: Remote
Travel Requirement: Occasional travel (approximately 10%)
Role Type: Full-Time
Domain: Artificial Intelligence, Cybersecurity, Agentic AI, Application Security

Role Overview

We are seeking an experiencedAI Security Architecture Manager with strong full-stack development expertise and a deep understanding of Agentic AI architecture, AI security, and cybersecurity risk management. The ideal candidate will design and implement intelligent, autonomous security solutions that continuously identify, assess, prioritize, and remediate software vulnerabilities through an AI-driven vulnerability detection and remediation flywheel.

The role combines hands-on full-stack engineering, Agentic AI solution architecture, cybersecurity engineering, and technical leadership to build secure, scalable, and reliable AI-powered security platforms. The successful candidate will leverage AI agents, large language models (LLMs), security automation, and feedback-driven learning mechanisms to improve vulnerability management and reduce remediation timelines.

Key Responsibilities

1. Agentic AI Architecture and Solution Design

• Design and implement Agentic AI architectures for automated vulnerability detection, analysis, prioritization, and remediation.
• Develop multi-agent workflows using LLMs, AI orchestration frameworks, tools, APIs, and enterprise security systems.
• Build autonomous and human-in-the-loop workflows with appropriate approval gates and escalation mechanisms.
• Define agent communication patterns, task orchestration, memory management, tool integration, and workflow coordination.
• Establish architectural standards for scalability, reliability, observability, and secure AI operations.

2. Vulnerability Detection and Remediation Flywheel

• Develop an AI-driven security lifecycle covering vulnerability discovery, contextual analysis, risk prioritization, remediation planning, validation, and continuous improvement.
• Integrate static application security testing (SAST), dynamic application security testing (DAST), software composition analysis (SCA), secrets scanning, and code security tools.
• Enable AI agents to analyze source code, identify root causes, recommend fixes, and generate remediation patches.
• Integrate remediation workflows with source control systems, CI/CD pipelines, issue trackers, and developer platforms.
• Implement automated testing, security validation, regression checks, and controlled pull-request generation.
•