• Support the Onit security function during US Central Time business hours. • Implement and manage cloud-native security tools and third-party solutions for threat detection and incident response. • Define, maintain, and execute the Incident Response plan, investigating and resolving incident escalations. • Perform regular risk assessments and vulnerability scans of cloud infrastructure, ensuring timely remediation. • Collaborate with Dev, DevOps, and Infra teams to remediate identified vulnerabilities, discuss security best practices, and assist with security incident response. • Analyze EDR alerts and logs to identify potential security incidents, taking appropriate action. • Continuously evaluate and implement security tools and practices to enhance the security posture of the Onit environment. • Assist with application security reviews and threat modeling. • Assist with security awareness programs for employees regarding security best practices

• Implement and maintain strong authentication protocols, including multi-factor authentication (MFA) for admin and employee accounts. • Enforce role-based access controls (RBAC) to limit access to sensitive systems and data based on job responsibility. • Monitor security logs and alerts to detect unauthorized access or suspicious activity. • Regularly update and patch website CMS, plugins, payment gateways, and related software to eliminate vulnerabilities. • Implement encryption of sensitive data both at rest and in transit (SSL certificates, AES-256 encryption, etc.). • Develop and maintain data retention and disposal policies, ensuring secure deletion of data no longer needed. • Maintain compliance with PCI-DSS standards for payment security and privacy regulations such as GDPR, HIPAA, or CCPA, as applicable. • Oversee the security of payment processing platforms, ensuring tokenization, end-to-end encryption, and fraud detection tools are in place. • Coordinate with payment gateway providers to maintain updated security certifications and compliance. • Schedule and conduct periodic vulnerability scans, penetration tests, and audits to proactively identify and address security gaps. • Collaborate with external security consultants or ethical hackers for advanced penetration testing. • Develop and maintain an incident response plan and lead response efforts for any suspected or actual breaches. • Provide security awareness training to staff regarding password hygiene, phishing, data handling, and access protocols. • Utilize real-time monitoring tools to oversee server logs, network traffic, and application access. • Implement proactive alerts for suspicious activities and maintain a dashboard of security metrics.

• Architect, Build, and Optimize ML Systems: Develop and deploy robust ML models that deliver high-impact results for real-world applications. • Training Pipeline Development: Design and implement efficient, scalable pipelines to train and retrain ML models, ensuring they meet business needs. • Fine-Tuning Large Language Models (LLMs): Continuously fine-tune LLMs to align with specific enterprise requirements, enhancing accuracy, relevance, and performance. • Feedback Systems Design: Implement and refine feedback loops to iteratively improve the effectiveness of ML models over time. • Cross-Functional Collaboration: Work closely with product and business teams to understand and translate requirements into ML solutions that provide tangible outcomes. • Stay Current with ML Advancements: Keep up with the latest in ML research and best practices, applying insights to our ML infrastructure to ensure it remains at the cutting edge. • Mentorship and Knowledge Sharing: Guide and mentor junior team members, fostering a culture of continuous improvement and technical growth. • Technical Communication: Clearly and effectively communicate ML methodologies, results, and insights to non-technical stakeholders.

• Architect, Build, and Optimize ML Systems: Develop and deploy robust ML models that deliver high-impact results for real-world applications. • Training Pipeline Development: Design and implement efficient, scalable pipelines to train and retrain ML models, ensuring they meet business needs. • Fine-Tuning Large Language Models (LLMs): Continuously fine-tune LLMs to align with specific enterprise requirements, enhancing accuracy, relevance, and performance. • Feedback Systems Design: Implement and refine feedback loops to iteratively improve the effectiveness of ML models over time. • Cross-Functional Collaboration: Work closely with product and business teams to understand and translate requirements into ML solutions that provide tangible outcomes. • Stay Current with ML Advancements: Keep up with the latest in ML research and best practices, applying insights to our ML infrastructure to ensure it remains at the cutting edge. • Mentorship and Knowledge Sharing: Guide and mentor junior team members, fostering a culture of continuous improvement and technical growth. • Technical Communication: Clearly and effectively communicate ML methodologies, results, and insights to non-technical stakeholders.