AI Security Engineer III - Agentic AI & Security Automation

We are seeking an innovative and hands-on AI Security Engineer to design, develop, and implement AI-powered solutions that transform traditional cybersecurity processes through intelligent automation, agentic AI, and secure software engineering.

This role will primarily focus on developing autonomous and semi-autonomous AI agents to identify, analyze, correlate, and prioritize security vulnerabilities; generate remediation recommendations; automate secure code and dependency fixes; validate proposed changes; create GitHub pull requests; and proactively engage application owners through intelligent notification and escalation workflows.

The engineer will build AI-driven capabilities to streamline vulnerability remediation across application code, APIs, software dependencies, container images, and cloud environments. A key objective is to reduce manual remediation effort, accelerate vulnerability closure, improve reporting accuracy, and enable application teams to address security risks more efficiently.

The engineer will also contribute to AI-driven innovations across Cryptographic Metadata, Automated Assurance, and Cryptographic Lifecycle Visibility, improving security posture, inventory accuracy, compliance, and proactive risk management.

The ideal candidate is a strong Python developer with practical experience in AI/LLM technologies, AI agent development, REST API integrations, GitHub automation, CI/CD pipelines, and secure software development. The candidate should be comfortable building production-quality automation solutions and applying emerging AI capabilities to solve real-world cybersecurity challenges.

• AI Agent Development & Innovation: Design, develop, and deploy intelligent AI agents using LLMs, agentic AI frameworks, retrieval-augmented generation (RAG), tool/function calling, and workflow orchestration to automate vulnerability management and cryptographic security processes.
• AI-Driven Vulnerability Detection & Remediation: Develop AI-powered solutions to analyze application code, GitHub repositories, dependencies, APIs, and container images; identify security vulnerabilities; generate secure remediation recommendations; and automate code and dependency fixes.
• Automated Code Fixes & GitHub Integration: Build intelligent agents capable of generating remediation code, updating vulnerable dependencies, executing automated tests and security validations, and creating GitHub pull requests for application owner review and approval.
• Intelligent Workflow Automation: Implement automated pull request monitoring, application owner notifications, remediation tracking, and escalation workflows. For example, trigger configurable email or collaboration-tool reminders when remediation pull requests remain unreviewed or unmerged beyond defined thresholds, such as 24 hours.
• AI-Driven Vulnerability Triage & Correlation: Develop AI-powered workflows to correlate findings, security scanners, and application repositories; identify potential duplicate or stale findings; analyze severity reclassifications; and distinguish actionable vulnerabilities from reporting discrepancies.
• Automated Remediation Validation: Develop workflows to validate remediation effectiveness through automated testing, security rescans, deployment verification, and vulnerability status reconciliation. Ensure findings are accurately tracked through remediation and closure.
• Cryptographic Metadata & Discovery: Develop AI-powered capabilities to discover cryptographic algorithms, certificates, keys, libraries, configurations, and dependencies across applications and repositories, improving inventory accuracy and risk visibility.
• Automated Cryptographic Assurance: Build intelligent solutions that evaluate cryptographic implementations against enterprise security policies, identify noncompliant configurations, assess cryptographic risks, and recommend remediation actions.
• Cryptographic Lifecycle Intelligence: Develop proactive monitoring and AI-driven workflows to identify expiring certificates, deprecated algorithms, key rotation requirements, and cryptographic migration opportunities.
• Security Platform & API Integration: Integrate AI agents with GitHub APIs, vulnerability APIs, AI Harness assessment results, vulnerability management platforms, and enterprise security tools to automate security analysis, risk prioritization, remediation workflows, and reporting.
• AI-Powered Security Insights & Reporting: Develop automated dashboards, vulnerability trend analysis, risk prioritization, remediation recommendations, and application owner notifications using findings from multiple security data sources. Enable visibility into new vulnerabilities, severity reclassifications, remediation progress, and outstanding security risks.
• AI Agent Reliability & Security Controls: Implement secure AI agent workflows with appropriate human approval, least-privilege access, audit logging, input/output validation, and safeguards against prompt injection or unauthorized actions. Evaluate AI-generated remediation recommendations for correctness, security, and reliability before implementation.
• Engineering & Collaboration: Write maintainable, reusable, and well-tested Python code; develop scalable automation components; integrate solutions into CI/CD pipelines; and collaborate with security engineering, application development, DevSecOps, and infrastructure teams to deploy reliable AI-driven security solutions.
• Bachelor's degree in Computer Science, Software Engineering, Cybersecurity, Artificial Intelligence, or a related field, or equivalent practical experience.
• 2–4 years of hands-on experience in software development, automation engineering, DevSecOps, security engineering, or related technical roles.
• Strong programming skills in Python, including scripting, REST API integrations, JSON processing, data manipulation, error handling, automated testing, and development of reusable automation components.
• Practical experience developing applications or automation solutions using Generative AI and Large Language Models (LLMs), including prompt engineering, tool/function calling, structured outputs, and AI agent workflows.
• Familiarity with AI agent frameworks and orchestration technologies such as LangChain, LangGraph or equivalent frameworks.
• Experience working with GitHub, Git workflows, pull requests, repository automation, and CI/CD pipelines.
• Understanding of software security fundamentals, vulnerability identification, secure coding practices, dependency management, and vulnerability remediation workflows.
• Working knowledge of common application security vulnerabilities and remediation approaches involving APIs, open-source dependencies, container images, and application code.
• Familiarity with security scanning tools, vulnerability management platforms, or CTEM concepts, including vulnerability prioritization, risk scoring, remediation tracking, and validation.
• Experience with REST API integrations, scheduled automation, event-driven workflows, and integration of multiple enterprise systems.
• Understanding of secure software development practices, including code reviews, unit testing, automated security testing, and deployment validation.
• Awareness of AI application security considerations, including prompt injection, sensitive-data handling, least-privilege access, human-in-the-loop approval, and validation of AI-generated outputs.
• Basic knowledge of cryptographic security concepts, including encryption algorithms, TLS, digital certificates, cryptographic libraries, and key management.
• Strong analytical, troubleshooting, and problem-solving skills, with the ability to translate cybersecurity requirements into practical engineering and automation solutions.
• Ability to collaborate effectively with cross-functional engineering, application development, DevSecOps, and cybersecurity teams.