Recently postedTechnology

AI Security Engineer Vulnerability Management & Security Operations

Tech Genius, IncDallas, TX🇺🇸United States

Quick overview

SeniorityMid Senior

Work modeOn Site

LocationDallas, TX, United States

Stakeholder ManagementGPTLLMPythonGenerative AI

Area	Requirement
Education	Bachelor s degree in Computer Science, Computer Engineering, Technology, Information Systems (CIS/MIS), Engineering, or a related technical discipline or equivalent experience/training
Vulnerability management	3 5 years of experience
Cybersecurity engineering	3 5+ years of experience
SDLC	3 5+ years designing, developing, and implementing large-scale applications in hosted production environments
Professional/open-source experience	3 5+ years of professional, design, and open-source experience
Preferred education	Master s degree in a related technical discipline
Preferred SDLC	5 years of full SDLC experience
Preferred AI experience	Experience developing or implementing generative-AI/LLM solutions using OpenAI GPT, Google Gemini, Llama, or similar
Preferred certification	Python programming or Artificial Intelligence certification is a plus

Onsite Interview in Dallas, TX

Mandatory Skills

Vulnerability management

Vulnerability identification, triage, prioritization, remediation tracking, and verification
Risk-based vulnerability management across applications, infrastructure, cloud, and production environments
Security scanning, patch management, configuration review, and exposure management
Reporting vulnerability trends, remediation status, compliance gaps, and KPIs to stakeholders

Cybersecurity engineering and SOC

Threat detection, monitoring, incident identification, containment, and remediation
Design and execution of advanced detection and incident-response protocols
SOC tooling, processes, operational governance, and continuous improvement
Secure communication protocols, data protection, and infrastructure-security integration
Coordination of physical-security measures with cybersecurity controls

Secure SDLC and application security

Secure design, development, deployment, and maintenance of large-scale hosted applications
Threat modeling, secure coding, code review, dependency management, and secrets management
Security controls embedded across planning, development, testing, deployment, and operations
Familiarity with common application risks such as injection, broken access control, SSRF, insecure deserialization, and supply-chain risks

AI/LLM security

Security assessment of generative-AI and LLM-based applications
Prompt-injection risks, including direct and indirect injection
Data leakage, sensitive-data exposure, model/output validation, and unsafe tool use
AI-specific threat modeling, red-teaming, adversarial testing, and guardrails
Secure integration of LLMs such as OpenAI GPT, Google Gemini, and Llama into enterprise applications
Human oversight, auditability, and responsible-AI controls for AI-enabled workflows

Engineering, automation, and leadership

Programming/scripting Python is especially valuable for security automation, scanning, detection, and remediation workflows
Open-source contribution or professional design experience
Project planning, resource coordination, risk/issue management, and delivery accountability
Executive communication, compliance reporting, and stakeholder management

Mentoring cybersecurity professionals and improving team capability