Senior Cloud Security Engineer

BMO•Chicago, IL

About The Position

We are seeking an enthusiastic and passionate professional for a Senior Cloud, AI & Data Security Engineer role who wants to design and implement security solutions for systems and services across AWS, Azure, and AI/ML platforms. We need someone who can establish the highest standards that meet and exceed security governance solutions and practices, provide assurance to management and auditors, and ensure sustained protection by embedding controls in operational and DevOps (CI/CD) practices with a focus on automation. We are looking for someone who has a high level of technical security expertise and who takes seriously the responsibility of monitoring, detecting, protecting, and maintaining the security of data, AI/ML systems, cloud platforms, and networks. You are a leader with a strong technical background. You have demonstrated strength in: Developing and implementing secure cloud and AI/ML architectures using a risk-based cybersecurity and data privacy strategy Defining security patterns, roadmaps, and operating models that leverage collaboration Facilitating industry-standard information security governance Advising senior leadership on cybersecurity, AI risk, and privacy risks, threats, and investment strategies Documenting appropriate policies and procedures to manage information security risks, including those unique to AI/ML systems and sensitive data assets As a qualified candidate, you will be part of the team driving BMO's Cloud, AI, and Data Security implementation. As a member of this team, you should possess the ability to inspire yourself and all of our team. Based on your previous experiences, you will inject new knowledge and skills into an already high-performing team, thus elevating our efforts to new heights.

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

Nice To Haves

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)

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