AI/ML Expert

As a Cybersecurity and AI/ML Specialist, you will play a crucial role in designing, implementing, and maintaining security frameworks to ensure the integrity, confidentiality, and compliance of AI-driven solutions throughout their lifecycle. Your responsibilities will include: - Designing and maintaining structured guidelines and controls for securing AI systems, covering data protection, model security, and compliance requirements. - Evaluating and utilizing established frameworks such as Google's Secure AI Framework (SAIF), NIST AI Risk Management Framework, or the Framework for AI Cybersecurity Practices (FAICP) as references or baselines. - Identifying, assessing, and mitigating security risks specific to AI, including adversarial attacks, data poisoning, model inversion, and unauthorized access. - Conducting regular vulnerability assessments and penetration testing on AI models and data pipelines. - Ensuring data used in AI systems is encrypted, anonymized, and securely stored. - Implementing robust access controls (e.g., RBAC, ABAC, Zero Trust) for sensitive AI data and models. - Protecting AI models from tampering, theft, or adversarial manipulation during training and deployment. - Monitoring and logging AI system activity for anomalies or security incidents. - Developing and enforcing policies to ensure AI systems adhere to industry regulations, ethical standards, and organizational governance requirements. - Promoting transparency, explainability, and fairness in AI models. - Establishing real-time monitoring and advanced threat detection for AI systems. - Developing and maintaining an AI incident response plan for prompt mitigation and recovery. - Educating teams on AI security best practices and fostering a security-aware culture. - Collaborating with IT, data science, compliance, and business units to align AI security with organizational goals. In terms of qualifications, you should possess: Technical Skills: - Strong understanding of AI/ML concepts, architectures, and security challenges. - Strong programming skills in Python, R, or similar languages. - Strong experience in Google Cloud Platform (GCP) or equivalent. - Solid understanding of machine learning algorithms, neural networks, NLP, and computer vision. - Experience with cloud AI/ML services and deployment pipelines is a plus. - Experience with security frameworks (e.g., SAIF, NIST, FAICP) and regulatory compliance. - Proficiency in data protection techniques, encryption, and secure access management. - Familiarity with adversarial machine learning, model hardening, and input sanitization. - Knowledge of incident response, monitoring tools, and threat intelligence platforms. - Excellent communication and documentation skills for policy development and stakeholder engagement. Experience: - Bachelor's or Master's degree in computer science, Data Science, Engineering, or a related field. - 5 years in AI/ML roles, including hands-on model development and deployment. - Track record of delivering AI solutions that drive business value. Certifications: - Relevant certifications such as CAISF, AICERTs, AI for Cybersecurity Specialization or equivalent. - GCP Cloud certification or equivalent in AWS or Azure (preferred). - Cybersecurity certificates (preferred). As a Cybersecurity and AI/ML Specialist, you will play a crucial role in designing, implementing, and maintaining security frameworks to ensure the integrity, confidentiality, and compliance of AI-driven solutions throughout their lifecycle. Your responsibilities will include: - Designing and maintaining structured guidelines and controls for securing AI systems, covering data protection, model security, and compliance requirements. - Evaluating and utilizing established frameworks such as Google's Secure AI Framework (SAIF), NIST AI Risk Management Framework, or the Framework for AI Cybersecurity Practices (FAICP) as references or baselines. - Identifying, assessing, and mitigating security risks specific to AI, including adversarial attacks, data poisoning, model inversion, and unauthorized access. - Conducting regular vulnerability assessments and penetration testing on AI models and data pipelines. - Ensuring data used in AI systems is encrypted, anonymized, and securely stored. - Implementing robust access controls (e.g., RBAC, ABAC, Zero Trust) for sensitive AI data and models. - Protecting AI models from tampering, theft, or adversarial manipulation during training and deployment. - Monitoring and logging AI system activity for anomalies or security incidents. - Developing and enforcing policies to ensure AI systems adhere to industry regulations, ethical standards, and organizational governance requirements. - Promoting transparency, explainability, and fairness in AI models. - Establishing real-time monitoring and advanced threat detection for AI systems. - Developing and maintaining an AI incident response plan for prompt mitigation and recovery. - Educating teams on AI