Cybersecurity Solutions III Architect/Engineer

• Play a pivotal role in today’s rapidly evolving cybersecurity landscape by leading the integration and management of Artificial Intelligence (AI) and machine learning (ML) technologies into security solutions to combat sophisticated cyber threats.
• Provide oversight and ensure compliance with security policies and programs related to AI, including secure development lifecycles and vulnerability management processes.
• Lead or support cybersecurity risk assessments, audits, program development, and incident response exercises, especially those involving AI systems.
• Establish and maintain secure development environments within AI platforms and integrated tools.
• Work with security teams and legal/business stakeholders to operationalize new cybersecurity AI/ML legislation.
• Collaborate with cross-functional teams, including AI/ML developers, security architects, and business stakeholders.
• Develop training programs to raise awareness of AI security risks and mitigation options.
• Threat Modeling and Risk Assessment. Identifying potential vulnerabilities and weaknesses within the organization’s systems and infrastructure, assessing the associated risks, and developing strategies to mitigate those risks.
• Design and implement technical solutions (e.g., DLP, SIEM, endpoint monitoring) to support insider risk/threat detection, logging, and telemetry ingestion.
• Develop actionable security blueprints, principles, models, designs, standards, and guidelines to ensure information technology architecture and support is consistent, usable, secure, and adds value to the business.
• Tune and optimize system performance to reduce false positives and ensure that detection logic remains relevant to changing behaviors and environments.
• Support the secure deployment of behavior analytics models while ensuring that infrastructure and access controls meet privacy and governance requirements.
• Contribute to the engineering to automated response capabilities, including alerting, blocking, or throttling based on defined insider risk thresholds.

Internet Security, Internet Security, Machine Learning, Security Attacks, Artificial Intelligence (AI), Computer Security, Product Lifecycle, Security Compliance, Maintain Compliance, Artificial Intelligence (AI) Programming Languages, * Deep understanding of Artificial Intelligence (AI) and machine learning (ML) to develop, implement, and manage secure AI-driven solutions.

• Essential blended skillset that combines strong technical knowledge in cybersecurity and AI with effective leadership, communication, and strategic thinking abilities.
• Strong technical foundation. In-depth knowledge of computer networks, operating systems (Windows, Linux, UNIX), cloud computing (AWS, Azure), network security protocols (TCP/IP, DNS, HTTPS, etc.), cryptography, and database security.
• Experience with system integration and scripting, including APIs, log forwarding, and automation via Python, PowerShell, or Bash.
• Strong understanding of network architecture and endpoint telemetry, especially how insiders interact with systems in both on-prem and cloud environments.
• Understanding of common cyber threats, attack vectors, vulnerabilities, security frameworks (NIST, ISO 27001), and security technologies providing a proactive and data-driven approach to protection.
• Excellent written and verbal communication skills, the ability to articulate complex security concepts to technical and non-technical audiences, and strong teamwork skills.
• Awareness of privacy and compliance requirements including how to implement insider monitoring ethically and legally., * A minimum of eight (8) years’ relevant experience.
• A degree from an accredited College/University in the applicable field of services is required. If the indiviual’s degree is not in the applicable field then four additional years of related experience is required.
• Expertise in identifying and mitigating AI-specific vulnerabilities such as adversarial attacks, model poisoning, privacy concerns (e.g., data leakage), prompt injection, and bias detection.
• Experience with cloud-native security and data architectures (e.g., AWS, Azure, Google Cloud) and securing AI systems within those environments.
• Certifications focused on AI security, governance, and development.