Senior Cloud Security Engineer

- Design and implement cloud security controls that reduce risk and improve prevention, detection, and response capabilities. - Contribute to securing multi-cloud and hybrid environments across AWS, Azure, GCP, and on-premise infrastructure. - Implement security controls for AI/ML workloads, including protecting data pipelines, model services, and AI-integrated applications. - Identify and help mitigate AI-specific risks such as prompt injection, data poisoning, and model/data leakage. - Apply DevSecOps and Infrastructure-as-Code (IaC) practices to integrate security into CI/CD pipelines. - Partner with product and platform teams to implement secure architecture patterns and cloud security standards. - Utilize CNAPP platforms and related tools to identify and remediate risks across cloud, container, and AI environments. - Implement and maintain security controls for containerized environments, including Kubernetes cluster configuration, image scanning, and runtime protection. - Support monitoring, detection, and response capabilities, including integration with cloud-native telemetry and security tooling. - Participate in threat modeling and risk assessments (Attack Surface Management, Data Security Posture Management, etc.) for cloud-native and AI-enabled systems. - Develop and maintain automation solutions to improve security coverage and operational efficiency. - Deploy and manage infrastructure using Infrastructure-as-Code (IaC) tools and best practices. - Contribute to security initiatives and projects, helping deliver measurable improvements to the organization’s security posture. - Support security operations and internal service requests, contributing to continuous process improvement. - Bachelor’s degree or equivalent in Computer Science, Information Security, or related field. - Experience designing and securing cloud and hybrid environments (AWS, Azure, GCP, On-Premise). - Proficiency in one or more programming or scripting languages, with experience interacting with cloud APIs and automation workflows. - Strong understanding of cloud security fundamentals, including IAM, network security, encryption, and secure architecture design. - Experience implementing DevSecOps practices and securing Infrastructure-as-Code (IaC) workflows. - Experience deploying and securing container technologies (Kubernetes, Docker, EKS, GKE, AKS). - Understanding of security risks in AI/ML systems, including prompt injection, data poisoning, and model/data leakage. - Familiarity with data security principles in AI training and inference pipelines. - Experience implementing basic security controls, logging, and monitoring for AI-enabled services. - Awareness of AI security frameworks such as OWASP Top 10 for LLMs and NIST AI Risk Management Framework. - Experience using CNAPP platforms to identify and remediate cloud security risks. - Familiarity with IaC scanning, cloud security posture management, and runtime detection tools. - Understanding of security prevention, detection, and response concepts. - Experience building and securing scalable cloud architectures across application, network, and data layers. - Familiarity with serverless and event-driven architectures (e.g., AWS Lambda, GCP Cloud Functions, Azure Automation). - Relevant certifications (e.g., AWS, Azure, GCP, Security+) are a plus. - Experience working in multi-OS and distributed environments.