1 to 2 of 2 Contract Data Protection Jobs in Reading

secure operation of AI and ML systems. Responsibilities: Develop and own robust enterprise security architecture for AI/ML systems, CI/CD and data pipelines. Participate in enterprise-wide architecture initiatives for AI/ML. Understand the workflow and pipeline architectures of ML and deep learning workloads. Conduct … ML models, LLM and development of trustworthiness, safety, fairness, bias and inappropriateness detection, as well as adversarial test specification, development, and execution. Collaborate with data scientists, engineers, and other stakeholders to embed security controls and measures into AI/ML applications, models, algorithms, and data pipelines. Define and … enforce robust access controls, authentication mechanisms, and encryption standards to safeguard AI/ML systems, training data, provenance, and any sensitive data. Stay abreast of emerging AI security threats, trends, and technologies, evaluating their impact and proactively adapting security measures to mitigate potential risks. Provide technical guidance and support more »

secure operation of AI and ML systems. Responsibilities: Develop and own robust enterprise security architecture for AI/ML systems, CI/CD and data pipelines. Participate in enterprise-wide architecture initiatives for AI/ML. Understand the workflow and pipeline architectures of ML and deep learning workloads. Conduct … adversarial test specification, development, and execution. Define and enforce robust access controls, authentication mechanisms, and encryption standards to safeguard AI/ML systems, training data, provenance, and any sensitive data. Stay abreast of emerging AI security threats, trends, and technologies, evaluating their impact and proactively adapting security measures to … security tools, technologies, and frameworks to enhance the overall security posture of AI/ML and other business systems. Develop and maintain relationships between data scientists, data engineers, developers, operations (DevOps, DataOps, MLOps) and business unit leaders to govern and support scalability of the ML and AI initiatives. more »