Key skills
Cloud securityAWSMicrosoft AzureAI/ML securitySecurity architectureSecurity certifications CISSPSecurity certifications CCSPSecurity certifications CCSKSecurity certifications AWS Certified Security SpecialtySecurity certifications Azure Security Engineer AssociateAI security certifications CDAIAI security certifications CompTIA AI+Data securityCloud-native security tools AWS Security HubCloud-native security tools AWS GuardDutyCloud-native security tools Microsoft Defender for CloudCloud-native security tools Azure SentinelSecurity compliance frameworks CSA CCMSecurity compliance frameworks ISO 27001Security compliance frameworks ISO 27017Security compliance frameworks NIST CSFAI/ML development frameworks TensorFlowAI/ML development frameworks PyTorchAI/ML development frameworks SageMakerAI/ML development frameworks Azure MLMLOps pipeline securityGenerative AI security risksData loss prevention (DLP)Data classificationData access governanceData encryptionTokenizationKey managementData privacy regulations PIPEDAData privacy regulations GDPRData privacy regulations CCPACloud-based data platforms SnowflakeCloud-based data platforms DatabricksCloud-based data platforms AWS RedshiftCloud-based data platforms Azure SynapseNetworking protocolsPacket analysis tools WiresharkPacket analysis tools Burp SuitePacket analysis tools nmapPacket analysis tools NessusPacket analysis tools MetasploitCryptographyIdentityAccess Management (IAM)Scriptingprogramming Pythonprogramming PowerShellprogramming Bashprogramming Node.jsAPI/webhook developmentInfrastructure as Code (IaC) security scanning tools CheckovInfrastructure as Code (IaC) security scanning tools tfsecInfluencing decision-makingResearchAnalytical skillsRelationship-buildingPythonBashPowerShellAIMachine LearningMLGenerative AIOpenAIPyTorchTensorFlowHugging FaceMLOpsData LakeAnalyticsSnowflakeRedshiftDatabricksNode.jsAmazon Web ServicesAzureS3IAMSageMakerPrismaOAuthSAMLSSOSaaSCI/CDLeadershipRisk ManagementCommunicationTime ManagementOWASPCloud Security
About this role
BMO U.S. is seeking a Senior Cloud Security Engineer who will design and implement security solutions for systems across AWS, Azure, and AI/ML platforms. The role involves developing secure cloud architectures, advising on cybersecurity risks, and ensuring compliance with security governance practices.
Responsibilities:
- Assess, design, implement, automate, and document security solutions, controls, and processes for Amazon Web Services (AWS) and Microsoft Azure cloud platforms
- Develop and maintain security patterns for cloud platforms and services; assess all cloud patterns to ensure adherence to best security practices and controls
- Design and implement security baseline controls for Cloud Services for integration into the CI/CD process
- Build and deliver policies as code, automating security controls and best practices
- Review and approve code and changes with security implications (e.g., IAM Roles and Policies, Security Groups, etc.)
- Be the cloud security subject matter expert for the Cloud Engineering group and its partners in any IaaS, PaaS, and SaaS implementations
- Define and implement a security framework for AI/ML systems, covering the full model lifecycle from data ingestion and training to deployment and monitoring
- Assess and mitigate AI-specific threats including adversarial attacks, model inversion, data poisoning, prompt injection, and model theft
- Evaluate and secure AI/ML platforms and tools (e.g., Amazon SageMaker, Azure Machine Learning, Hugging Face, OpenAI APIs) against organizational risk standards
- Collaborate with data science and AI engineering teams to integrate security controls into MLOps pipelines, ensuring model integrity, access controls, and auditability
- Monitor emerging AI threat landscapes and regulatory developments (e.g., EU AI Act, NIST AI RMF) and translate these into actionable organizational controls
- Implement and manage data security posture management (DSPM) tools to continuously monitor sensitive data exposure across cloud environments
- Establish controls for structured and unstructured data stores, including databases, data lakes, data warehouses (e.g., Snowflake, AWS S3, Azure Data Lake), and file sharing platforms
- Drive the adoption of data-centric security practices within application development and analytics teams
- Provide subject matter expertise on architecture, authentication, and systems security based on a clear understanding of the engineering stack, services, and data flow
- Lead focused and continuous cybersecurity risk assessments of new and existing technologies - including AI/ML systems and data platforms - to identify risks and appropriate controls that balance security and operability
- Provide effective and pragmatic cybersecurity guidance upfront in major technology projects to enable the business to innovate securely
- Assist in the investigation and remediation of security incidents and issues, including those involving AI model compromise or data breaches
- Work closely with Information Security, product, and software development teams to assess cybersecurity risk and recommend solutions in cloud, AI, and data environments
Requirements:
- A university degree in Engineering, Computer Science, Information Technology, or a related field
- 7-10 years of experience developing and implementing security architectures and/or engineering, with demonstrated breadth across cloud, data, and/or AI security domains
- Security certifications such as CISSP, CCSP, CCSK, or any Cloud Security Specialty certification (e.g., AWS Certified Security Specialty, Microsoft Certified: Azure Security Engineer Associate)
- Demonstrated knowledge of cloud architecture, cloud operations, cloud-based identity and access management, security automation, and orchestration
- Extensive experience with cloud-native security solutions and tools (e.g., AWS Security Hub, AWS GuardDuty, Microsoft Defender for Cloud, Azure Sentinel)
- Knowledge of technical security control environments and compliance frameworks including CSA CCM, ISO 27001, ISO 27017, and NIST CSF
- Working knowledge of AI/ML development frameworks and platforms (e.g., TensorFlow, PyTorch, SageMaker, Azure ML) and associated security risks
- Familiarity with the OWASP Top 10 for LLMs, MITRE ATLAS, and NIST AI Risk Management Framework (AI RMF)
- Understanding of MLOps pipeline security, including securing model registries, feature stores, training environments, and inference endpoints
- Knowledge of Generative AI security risks, including prompt injection, jailbreaking, data leakage via LLMs, and supply chain risks in AI model dependencies
- Experience implementing data loss prevention (DLP), data classification, and data access governance solutions in enterprise environments
- Knowledge of DSPM tools and practices
- Understanding of data encryption at rest and in transit, tokenization, and key management for large-scale data environments
- Familiarity with data privacy regulations (e.g., PIPEDA, GDPR, CCPA) and their technical implementation requirements
- Experience securing cloud-based data platforms such as Snowflake, Databricks, AWS Redshift, Azure Synapse, or equivalent
- Firm grasp of networking protocols and operations; comfortable with packet analysis tools such as Wireshark, Burp Suite, nmap, Nessus, and Metasploit
- Knowledge of theoretical and applied cryptography, key management, and cryptographic algorithms (RSA, AES, TLS, PKI, etc.)
- Knowledge of Identity and Access Management (IAM) concepts including SSO, SAML, federated identity, RBAC, and OAuth/OIDC
- Strong scripting and programming skills with experience in Python, PowerShell, Bash, Node.js, and API/webhook development
- Experience with Infrastructure as Code (IaC) security scanning tools (e.g., Checkov, tfsec, Prisma Cloud)
- Demonstrable internal and external relationship-building skills with the ability to clearly articulate complex security concepts across a diverse corporate culture
- Ability to lead in-depth workshops across a broad range of topics including cloud compliance, AI risk, and data governance
- Strong ability to influence decision-making at senior leadership levels
- Strong interpersonal, communication, and leadership skills
- A critical thinker with strong research, analytical, and problem-solving skills
- Self-motivated with a positive attitude and an ability to work independently and within a team
- Ability to communicate complex technical concepts to a broad range of internal and external stakeholders, including business, legal, compliance, and technology leaders
- Strong time management skills with the ability to manage multiple workstreams and mentor less experienced team members
- Emerging/preferred: Certifications or demonstrated knowledge in AI security (e.g., CDAI, CompTIA AI+, or equivalent vendor-specific AI security training) or data security (e.g., CDPSE, CIPP)