Principal Cloud Security Engineer

Salary: $167000 - 213000 per year Requirements: We require a university degree in Engineering, Computer Science, Information Technology, or a related discipline. We require 7 to 10 years of experience building and applying security architectures or engineering solutions, with breadth across cloud, data, and/or AI security. We require recognized security certifications such as CISSP, CCSP, CCSK, or a cloud security specialty credential such as AWS Certified Security Specialty or Microsoft Certified: Azure Security Engineer Associate. We value emerging knowledge or certifications in AI security, such as CDAI, CompTIA AI+, or equivalent vendor-specific training, as well as data security credentials such as CDPSE or CIPP. We require strong knowledge of cloud architecture, cloud operations, identity and access management, security automation, and orchestration. We require hands-on experience with cloud-native security tools and services such as AWS Security Hub, AWS GuardDuty, Microsoft Defender for Cloud, and Azure Sentinel. We require familiarity with security and compliance frameworks including CSA CCM, ISO 27001, ISO 27017, and NIST CSF. We require working knowledge of AI/ML platforms and development frameworks such as TensorFlow, PyTorch, SageMaker, and Azure ML, along with the related security considerations. We require understanding of AI threat models, including prompt injection, model theft, model inversion, data poisoning, adversarial attacks, and supply chain risk. We require familiarity with the OWASP Top 10 for LLMs, MITRE ATLAS, and the NIST AI Risk Management Framework. We require experience with data loss prevention, data classification, data access governance, and DSPM practices. We require knowledge of encryption at rest and in transit, tokenization, and key management in large-scale data environments. We require awareness of privacy laws such as PIPEDA, GDPR, and CCPA and how to implement technical controls aligned to them. We require experience securing cloud-based data platforms such as Snowflake, Databricks, AWS Redshift, or Azure Synapse. We require solid networking knowledge and familiarity with tools such as Wireshark, Burp Suite, nmap, Nessus, and Metasploit. We require understanding of cryptography, key management, and common algorithms and protocols such as RSA, AES, TLS, and PKI. We require strong IAM knowledge, including SSO, SAML, federated identity, RBAC, and OAuth/OIDC. We require strong scripting and development capability in Python, PowerShell, Bash, Node.js, and API or webhook development. We require experience using Infrastructure as Code security scanning tools such as Checkov, tfsec, or Prisma Cloud. We require excellent communication, relationship-building, and facilitation skills, with the ability to explain complex security topics clearly across a broad organization. We require a self-starter mindset, the ability to manage multiple priorities, and the confidence to lead workshops and cross-functional discussions. Responsibilities: We assess, design, implement, automate, and document security solutions, controls, and processes for AWS and Microsoft Azure environments. We create and maintain cloud security patterns and verify that cloud designs follow best practices and control requirements. We build security baseline controls for cloud services and embed them into CI/CD pipelines. We develop policies as code and automate security safeguards and standards. We review and approve security-relevant code and configuration changes, including IAM roles, policies, and security groups. We serve as the cloud security subject matter expert for the Cloud Engineering team and its partners across IaaS, PaaS, and SaaS implementations. We define and apply a security framework for AI/ML systems across the full model lifecycle, from data intake and training through deployment and monitoring. We identify and reduce AI-specific risks such as adversarial attacks, data poisoning, prompt injection, model theft, and model inversion. We evaluate and secure AI/ML platforms and tools against our organizational risk standards. We partner with data science and AI engineering teams to integrate security controls into MLOps workflows and preserve model integrity, access control, and auditability. We monitor evolving AI threats and regulatory developments and translate them into practical internal controls. We implement and operate DSPM tooling to track sensitive data exposure across cloud environments. We establish protections for structured and unstructured data stores, including databases, data lakes, data warehouses, and file-sharing platforms. We promote data-centric security practices within application development and analytics teams. We provide architecture, authentication, and systems security guidance based on a strong understanding of the engineering stack, services, and data flows. We lead ongoing cybersecurity risk assessments for new and existing technologies, including AI/ML systems and